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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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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Small Autonomous Aircraft Servo Health Monitoring

Small air vehicles offer challenging power, weight, and volume constraints when considering implementation of system health monitoring technologies. In order to develop a testbed for monitoring the health and integrity of control surface servos and linkages, the Autonomous Aircraft Servo Health Monitoring system has been designed for small Uninhabited Aerial Vehicle (UAV) platforms to detect problematic behavior from servos and the air craft structures they control, This system will serve to verify the structural integrity of an aircraft's servos and linkages and thereby, through early detection of a problematic situation, minimize the chances of an aircraft accident. Embry-Riddle Aeronautical University's rotary-winged UAV has an Airborne Power management unit that is responsible for regulating, distributing, and monitoring the power supplied to the UAV's avionics. The current sensing technology utilized by the Airborne Power Management system is also the basis for the Servo Health system. The Servo Health system measures the current draw of the servos while the servos are in Motion in order to quantify the servo health. During a preflight check, deviations from a known baseline behavior can be logged and their causes found upon closer inspection of the aircraft. The erratic behavior nay include binding as a result of dirt buildup or backlash caused by looseness in the mechanical linkages. Moreover, the Servo Health system will allow elusive problems to be identified and preventative measures taken to avoid unnecessary hazardous conditions in small autonomous aircraft.

Quintero, Steven↗
NASA NTRS2009

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, satellite vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed, or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery.

Satellites↗
NASA NTRS2012

General Purpose Data-Driven System Monitoring for Space Operations

Modern space propulsion and exploration system designs are becoming increasingly sophisticated and complex. Determining the health state of these systems using traditional methods is becoming more difficult as the number of sensors and component interactions grows. Data-driven monitoring techniques have been developed to address these issues by analyzing system operations data to automatically characterize normal system behavior. The Inductive Monitoring System (IMS) is a data-driven system health monitoring software tool that has been successfully applied to several aerospace applications. IMS uses a data mining technique called clustering to analyze archived system data and characterize normal interactions between parameters. This characterization, or model, of nominal operation is stored in a knowledge base that can be used for real-time system monitoring or for analysis of archived events. Ongoing and developing IMS space operations applications include International Space Station flight control, spacecraft vehicle system health management, launch vehicle ground operations, and fleet supportability. As a common thread of discussion this paper will employ the evolution of the IMS data-driven technique as related to several Integrated Systems Health Management (ISHM) elements. Thematically, the projects listed will be used as case studies. The maturation of IMS via projects where it has been deployed or is currently being integrated to aid in fault detection will be described. The paper will also explain how IMS can be used to complement a suite of other ISHM tools, providing initial fault detection support for diagnosis and recovery

Space Propulsion↗
NASA NTRS2011

Integrated Software Health Management for Aircraft GN and C

Modern aircraft rely heavily on dependable operation of many safety-critical software components. Despite careful design, verification and validation (V&V), on-board software can fail with disastrous consequences if it encounters problematic software/hardware interaction or must operate in an unexpected environment. We are using a Bayesian approach to monitor the software and its behavior during operation and provide up-to-date information about the health of the software and its components. The powerful reasoning mechanism provided by our model-based Bayesian approach makes reliable diagnosis of the root causes possible and minimizes the number of false alarms. Compilation of the Bayesian model into compact arithmetic circuits makes SWHM feasible even on platforms with limited CPU power. We show initial results of SWHM on a small simulator of an embedded aircraft software system, where software and sensor faults can be injected.

Schumann, Johann↗
NASA NTRS2016

Evaluation of Different MODIS AOD Retrieval Algorithms for PM (sub 2.5) Estimation in the Western, Midwestern and Southeastern United States with Implications for Public Health

This study was part of the research activities of the Center for Applied Atmospheric Research and Education (CAARE) funded by the NASA MUREP (Minority University Research and Education Project) Institutional Research Opportunity (MIRO) Program. Satellite measurements of Aerosol Optical Depth (AOD) have been shown to be correlated with ground measurements of fine particulate matter less than 2.5 microns PM (sub 2.5), which in turn has been linked to respiratory and heart diseases. The strength of the correlation between AOD and PM (sub 2.5) varies for different AOD retrieval algorithms and geographic regions. We evaluated several Moderate Resolution Imaging Spectrometer (MODIS) AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target versus Deep Blue), Collections (5.1 versus 6) and spatial resolutions (10-kilometers versus 3-kilometers) for cities in the Western, Midwestern and Southeastern U.S. We developed and validated PM (sub 2.5) prediction models using remotely-sensed AOD data, which were improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind speed, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the predictive power of all the PM (sub 2.5) models, especially in the Western U.S. Temperature, relative humidity and wind speed were the most significant meteorological variables throughout the year in the Western U.S. Wind speed was the most significant meteorological variable for the cold season while temperature was the most significant variable for the warm season in the Midwestern and Southeastern U.S. Our study re-establishes the connection between PM (sub 2.5) and public health concerns including respiratory and cardiovascular diseases (asthma, high blood pressure, coronary heart disease, heart attack, and stroke). Using PM (sub 2.5) data and health data from the Centers for Disease Control and Prevention (CDC)'s Behavioral Risk Factor Surveillance System (BRFSS), our statistical analysis showed that heart attack and stroke occurrences had the strongest correlations with PM (sub 2.5).

Environmental Public Health↗
DOE OSTI2024

Near-infrared nanosensors enable optical imaging of oxytocin with selectivity over vasopressin in acute mouse brain slices

Oxytocin plays a critical role in regulating social behaviors, yet our understanding of its function in both neurological health and disease remains incomplete. Real-time oxytocin imaging probes with spatiotemporal resolution relevant to its endogenous signaling are required to fully elucidate oxytocin’s role in the brain. Herein, we describe a near-infrared oxytocin nanosensor (nIROXT), a synthetic probe capable of imaging oxytocin in the brain without interference from its structural analogue, vasopressin. nIROXT leverages the inherent tissue-transparent fluorescence of single-walled carbon nanotubes (SWCNT) and the molecular recognition capacity of an oxytocin receptor peptide fragment to selectively and reversibly image oxytocin. We employ these nanosensors to monitor electrically stimulated oxytocin release in brain tissue, revealing oxytocin release sites with a median size of 3 µm in the paraventricular nucleus of C57BL/6 mice, which putatively represents the spatial diffusion of oxytocin from its point of release. These data demonstrate that covalent SWCNT constructs, such as nIROXT, are powerful optical tools that can be leveraged to measure neuropeptide release in brain tissue.

Science & Technology - Other Topics↗
NASA NTRS2002

Analysis of Salinity Intrusion in the San Francisco Bay-Delta using a GA- Optimized Neural Net, and Application of the Model to Prediction in the Elkhorn Slough Habitat

The San Francisco Bay Delta is a large hydrodynamic complex that incorporates the Sacramento and San Joaquin Estuaries, the Burman Marsh, and the San Francisco Bay proper. Competition exists for the use of this extensive water system both from the fisheries industry, the agricultural industry, and from the marine and estuarine animal species within the Delta. As tidal fluctuations occur, more saline water pushes upstream allowing fish to migrate beyond the Burman Marsh for breeding and habitat occupation. However, the agriculture industry does not want extensive salinity intrusion to impact water quality for human and plant consumption. The balance is regulated by pumping stations located alone the estuaries and reservoirs whereby flushing of fresh water keeps the saline intrusion at bay. The pumping schedule is driven by data collected at various locations within the Bay Delta and by numerical models that predict the salinity intrusion as part of a larger model of the system. The Interagency Ecological Program (IEP) for the San Francisco Bay/Sacramento-San Joaquin Estuary collects, monitors, and archives the data, and the Department of Water Resources provides a numerical model simulation (DSM2) from which predictions are made that drive the pumping schedule. A problem with this procedure is that the numerical simulation takes roughly 16 hours to complete a C:~ prediction. We have created a neural net, optimized with a genetic algorithm, that takes as input the archived data from multiple stations and predicts stage, salinity, and flow at the Carquinez Straits (at the downstream end of the Burman Marsh). This model seems to be robust in its predictions and operates much faster than the current numerical DSM2 model. Because the system is strongly tidal driven, we used both Principal Component Analysis and Fast Fourier Transforms to discover dominant features within the IEP data. We then filtered out the dominant tidal forcing to discover non-primary tidal effects, and used this to enhance the neural network by mapping input-output relationships in a more efficient manner. Furthermore, the neural network implicitly incorporates both the hydrodynamic and water quality models into a single predictive system. Although our model has not yet been enhanced to demonstrate improve pumping schedules, it has the possibility to support better decision-making procedures that may then be implemented by State agencies if desired. Our intention is now to use this model in the smaller Elkhorn Slough complex near Monterey Bay where no such hydrodynamic model currently exists. At the Elkhorn Slough, we are fusing the neural net model of tidally-driven flow with in situ flow data and airborne and satellite remote sensation data. These further constrain the behavior of the model in predicting the longer-term health and future of this vital estuary.

Thompson, David E.↗
NASA NTRS1996

The intersection of risk assessment and neurobehavioral toxicity

Neurobehavioral toxicology is now established as a core discipline of the environmental health sciences. Despite its recognized scientific prowess, stemming from its deep roots in psychology and neuroscience and its acknowledged successes, it faces additional demands and challenges. The latter, in fact, are a product of its achievements because success at one level leads to new and higher expectations. Now the discipline is counted upon to provide more definitive and extensive risk assessments than in the past. These new demands are the basis for the appraisals presented in the SGOMSEC 11 workshop. They extend beyond what would be offered in a primer of methodology. Instead, these appraisals are framed as issues into which what are usually construed as methodologies have been embedded.

NASA Discipline Environmental Health↗
NASA NTRS2008

Testing HyDE on ADAPT

The IVHM Project in the Aviation Safety Program has funded research in electrical power system (EPS) health management. This problem domain contains both discrete and continuous behavior, and thus is directly relevant for the hybrid diagnostic tool HyDE. In FY2007 work was performed to expand the HyDE diagnosis model of the ADAPT system. The work completed resulted in a HyDE model with the capability to diagnose five times the number of ADAPT components previously tested. The expanded diagnosis model passed a corresponding set of new ADAPT fault injection scenario tests with no incorrect faults reported. The time required for the HyDE diagnostic system to isolate the fault varied widely between tests; this variance was reduced by tuning HyDE input parameters. These results and other diagnostic design trade-offs are discussed. Finally, possible future improvements for both the HyDE diagnostic model and HyDE itself are presented.

Sweet, Adam↗
NASA NTRS2010

NASA Tech Briefs, October 2010

Topics covered include: Hybrid Architecture Active Wavefront Sensing and Control; Carbon-Nanotube-Based Chemical Gas Sensor; Aerogel-Positronium Technology for the Detection of Small Quantities of Organic and/or Toxic Materials; Graphene-Based Reversible Nano-Switch/Sensor Schottky Diode; Inductive Non-Contact Position Sensor; High-Temperature Surface-Acoustic-Wave Transducer; Grid-Sphere Electrodes for Contact with Ionospheric Plasma; Enabling IP Header Compression in COTS Routers via Frame Relay on a Simplex Link; Ka-Band SiGe Receiver Front-End MMIC for Transponder Applications; Robust Optimization Design Algorithm for High-Frequency TWTs; Optimal and Local Connectivity Between Neuron and Synapse Array in the Quantum Dot/Silicon Brain; Method and Circuit for In-Situ Health Monitoring of Solar Cells in Space; BGen: A UML Behavior Network Generator Tool; Platform for Post-Processing Waveform-Based NDE; Electrochemical Hydrogen Peroxide Generator; Fabrication of Single, Vertically Aligned Carbon Nanotubes in 3D Nanoscale Architectures; Process to Create High-Fidelity Lunar Dust Simulants; Lithium-Ion Electrolytes Containing Phosphorous-Based, Flame-Retardant Additives; InGaP Heterojunction Barrier Solar Cells; Straight-Pore Microfilter with Efficient Regeneration; Determining Shear Stress Distribution in a Laminate; Self-Adjusting Liquid Injectors for Combustors; Handling Qualities Prediction of an F-16XL-Based Reduced Sonic Boom Aircraft; Tele-Robotic ATHLETE Controller for Kinematics - TRACK; Three-Wheel Brush-Wheel Sampler; Heterodyne Interferometer Angle Metrology; Aligning Astronomical Telescopes via Identification of Stars; Generation of Optical Combs in a WGM Resonator from a Bichromatic Pump; Large-Format AlGaN PIN Photodiode Arrays for UV Images; Fiber-Coupled Planar Light-Wave Circuit for Seed Laser Control in High Spectral Resolution Lidar Systems; On Calculating the Zero-Gravity Surface Figure of a Mirror; Optical Modification of Casimir Forces for Improved Function of Micro- and Nano-Scale Devices; Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems; Core and Off-Core Processes in Systems Engineering; Digital Reconstruction Supporting Investigation of Mishaps; and Template Matching Approach to Signal Prediction.

Source record↗
NASA NTRSDate not supplied

Thermal Data-driven Model Reduction for Enhanced Battery Health Monitoring

Electric aviation faces a major challenge of avoiding potentially catastrophic consequences of the battery’s thermal runaway while keeping the weight of the battery low. Detection of early warning signals of battery failures requires accurate monitoring of the battery’s health throughout its lifespan. However, identifying the parameters of the battery from field data is notoriously difficult. We investigate this problem within the framework of modeling the temperature dynamics of a Li-ion cell during tests simulating loading in electric aircraft flights. It is found that the parameters of a higher-fidelity physics-based thermal model cannot be identified from the simulated flight data. To resolve this issue, we reduce the higher-fidelity thermal model to a model with fewer parameters. The resulting reduced-order model can predict temperature dynamics accurately and is identifiable throughout the cell’s lifespan which allows using the model’s parameters to monitor the state-of-health of the aging cell and detect anomalies in thermal behavior.

Li ion batteries↗
DOE OSTI2026

Corn grown on dredged sediments alters rat behavior in the elevated plus maze via the gut mycobiome

The use of marginal lands for growing food has increased worldwide. Many of these lands are supplemented with soil amendments to enhance their productivity, but they may also introduce contaminants depending on their origin. The use of sediments dredged from commercial waterways as a soil amendment has increased in recent years. However, there is significant concern regarding their potential to negatively affect public health by transferring contaminants, like metals, to the food supply. This study examined the behavior, microbiome and physiology of rats fed corn grown on dredged sediments with rats fed commercial feed corn and related those metrics to kernel metal content. Metal content did not differ between kernels grown on dredged sediments and feed corn for most metals except iron which was greater in commercial feed corn. Although there is some variability by animal response, animals fed corn grown on dredged sediments had a gut microbial community that generally promoted anxiety-like behavior while animals fed feed corn had a gut microbial community that promoted a more typical response. These findings suggest the effect of corn grown on dredged sediments on public health is complex and highlights the need for further investigation.

Rua, Megan [Wright State University, Dayton, OH]↗
DOE OSTI2024

The relationship between stress, anxiety and eating behavior among Chinese students: a cross-sectional study

Background The expansion of higher education and the growing number of college students have led to increased awareness of mental health issues such as stress, anxiety, and eating disorders. In China, the educational system and cultural expectations contribute to the stress experienced by college students. This study aims to clarify the role of anxiety as a mediator in the relationship between stress and eating behaviors among Chinese college students. Methods This study utilized data from the 2021 Psychology and Behavior Investigation of Chinese Residents, which included 1,672 college students under the age of 25. The analysis methods comprised descriptive statistics, t -tests, Pearson correlation analyses, and mediation effect analysis. Results The findings indicate that Chinese college students experience high levels of stress, with long-term stress slightly exceeding short-term stress. Both types of stress were positively correlated with increased anxiety and the adoption of unhealthy eating behaviors. Anxiety was identified as a significant mediator, accounting for 28.3% of the relationship between long-term stress and eating behavior (95% CI = 0.058–0.183). The mediation effect of short-term stress on eating behavior through anxiety was also significant, explaining 61.4% of the total effect (95% CI = 0.185–0.327). Conclusion The study underscores the importance of stress management and mental health services for college students. It recommends a comprehensive approach to reducing external pressures, managing anxiety, and promoting healthy eating behaviors among college students. Suggestions include expanding employment opportunities, providing career guidance, enhancing campus and societal support for holistic development, strengthening mental health services, leveraging artificial intelligence technologies, educating on healthy lifestyles, and implementing targeted health promotion programs.

Chai, Yulin↗
NASA NTRS2017

Mouse Behavior on ISS: The Emergence of a Distinctive, Organized Group Circling Behavior Unique to Spaceflight

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. Only a handful of papers have previously reported behavior of mice and rats in the weightless environment of space (Andreev-Andrievskiy, et al., 2013; Cancedda et al., 2012; Ronca et al., 2008). The Rodent Research Hardware and Operations Validation Mission (Rodent Research-1; RR1) utilized the Rodent Habitat (RH) developed at NASA Ames Research Center to fly mice on the ISS. Ten adult (16-week-old) female C57BL6J mice were launched on September 21st, 2014 in an unmanned Dragon Capsule, and spent 37 days in flight. Here we report group behavioral phenotypes of the RR1 Flight (FLT) and environment-matched Ground Control (GC) mice in the RH during this long duration flight. Video was recorded for 34 days on the ISS, permitting daily assessments of overall health and well being of the mice, and providing a valuable repository for detailed behavioral analysis. As compared to GC mice, RR1 FLT mice exhibited the same range of behaviors, including eating, drinking, exploration, self- and allogrooming, and social interactions at similar or greater levels of occurrence. Overall activity was greater in FLT as compared to GC mice, with spontaneous ambulatory behavior, including organized circling or race-tracking behavior that emerged within the first few days of flight following a common developmental sequence, comprising the primary dark cycle activity of FLT mice. Circling participation by individual mice persisted throughout the mission. Analysis of group behavior over mission days revealed recruitment of mice into the group phenotype, coupled with decreasing numbers of collisions between circling mice. This analysis provides insights into the behavior of mice in microgravity, and clear evidence for the emergence of a distinctive, organized group behavior unique to the weightless space environment. Supported by the NASA Rodent Research Project, Space Biology Program, and Space Life Sciences Training Program.

spaceflight↗
NASA NTRS2017

Mouse Behavior on ISS: The Emergence of Distinctive, Organized Group Circling Behavior Unique to Spaceflight

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. Only a handful of papers have previously reported behavior of mice and rats in the weightless environment of space (Andreev-Andrievskiy, et al., 2013; Cancedda et al., 2012; Ronca et al., 2008). The Rodent Research Hardware and Operations Validation Mission (Rodent Research-1; RR1) utilized the Rodent Habitat (RH) developed at NASA Ames Research Center to fly mice on the ISS. Ten adult (16-week-old) female C57BL6J mice were launched on September 21st, 2014 in an unmanned Dragon Capsule, and spent 37 days in flight. Here we report group behavioral phenotypes of the RR1 Flight (FLT) and environment-matched Ground Control (GC) mice in the RH during this long duration flight. Video was recorded for 34 days on the ISS, permitting daily assessments of overall health and well being of the mice, and providing a valuable repository for detailed behavioral analysis. As compared to GC mice, RR1 FLT mice exhibited the same range of behaviors, including eating, drinking, exploration, self- and allogrooming,and social interactions at similar or greater levels of occurrence. Overallactivity was greater in FLT as compared to GC mice, with spontaneous ambulatory behavior, including organized circling or race-tracking behavior that emerged within thefirst few days of flight following a common developmental sequence, comprising theprimary dark cycle activity of FLT mice. Circling participation by individual micepersisted throughout the mission. Analysis of group behavior over mission days revealed recruitment of mice into the group phenotype, coupled with decreasing numbers of collisions between circling mice. This analysis provides insights into the behavior of mice in microgravity, and clear evidence for the emergence of a distinctive,organized group behavior unique to the weightless space environment.

mice↗
NASA NTRS2017

EcAMSat and BioSentinel: Autonomous Bio Nanosatellites Addressing Strategic Knowledge Gaps for Manned Spaceflight Beyond LEO

Manned missions beyond low Earth orbit (LEO) require that several strategic knowledge gaps about the effects of space travel on the human body be addressed. NASA Ames Research Center has been the leader in developing autonomous bio nanosatellites, including past successful missions for GeneSat, PharmaSat, and O/OREOS, that tackled some of these issues. These nanosatellites provide in situ measurements, which deliver insight into the dynamic changes in cell behavior in microgravity. In this talk, two upcoming bio nanosatellites developed at Ames, the E. coli Antimicrobial Satellite (EcAMSat) and BioSentinel, will be discussed. Both satellites contain microfluidic systems that precisely deliver nutrients to the microorganisms stored within wells of fluidic cards. Each well, in turn, has its own 3-color LED and detector system which is used to monitor changes in metabolic activity with alamarBlue, a redox indicator, and the optical density of the cells. EcAMSat investigates the effects of microgravity on bacterial resistance to antimicrobial drugs, vital knowledge for understanding how to maintain the health of astronauts in long-term and beyond LEO spaceflight. The behavior of wild type and mutant uropathic E. coli will be compared in microgravity and with ground data to help understand the molecular mechanisms behind antibiotic resistance and how these phenotypes might change in space. BioSentinel seeks to directly measure the effects of space radiation on budding yeast S. cerevisiae, particularly double strand breaks (DSB). While hitching a ride on the SLS EM-1 mission (Orion's first unmanned mission to the moon) in 2018, BioSentinel will be kicked off and enter into a heliocentric orbit, becoming the first study of the effects of radiation on living organisms outside LEO since the Apollo program. The yeast are stored in eighteen independent 16-well microfluidic cards, which will be individually activated over the 12 month mission duration. In addition to the wild type and radiation-sensitive mutant strains, a BioSentinel strain of yeast has been developed, which requires a DSB to reactivate growth, thereby allowing for a direct measurement of DSBs caused by radiation. These two missions demonstrate the utility of using autonomous nanosatellites to address strategic knowledge gaps in the push to once again extend manned spaceflight beyond LEO.

Padgen, Michael R.↗
NASA NTRS2017

EcAMSat and BioSentinel: Autonomous Bio Nanosatellites Addressing Strategic Knowledge Gaps for Manned Spaceflight Beyond LEO

Manned missions beyond low Earth orbit (LEO) require that several strategic knowledge gaps about the effects of space travel on the human body be addressed. NASA Ames Research Center has been the leader in developing autonomous bio nanosatellites, including past successful missions for GeneSat, PharmaSat, and OOREOS, that tackled some of these issues. These nanosatellites provide in situ measurements, which deliver insight into the dynamic changes in cell behavior in microgravity. In this talk, two upcoming bio nanosatellites developed at Ames, the E. coli Antimicrobial Satellite (EcAMSat) and BioSentinel, will be discussed. Both satellites contain microfluidic systems that precisely deliver nutrients to the microorganisms stored within wells of fluidic cards. Each well, in turn, has its own 3-color LED and detector system which is used to monitor changes in metabolic activity with alamarBlue, a redox indicator, and the optical density of the cells. EcAMSat investigates the effects of microgravity on bacterial resistance to antimicrobial drugs, vital knowledge for understanding how to maintain the health of astronauts in long-term and beyond LEO spaceflight. The behavior of wild type and mutant uropathic E. coli will be compared in microgravity and with ground data to help understand the molecular mechanisms behind antibiotic resistance and how these phenotypes might change in space. BioSentinel seeks to directly measure the effects of space radiation on budding yeast S. cerevisiae, particularly double strand breaks (DSB). While hitching a ride on the SLS EM-1 mission (Orions first unmanned mission to the moon) in 2018, BioSentinel will be kicked off and enter into a heliocentric orbit, becoming the first study of the effects of radiation on living organisms outside LEO since the Apollo program. The yeast are stored in eighteen independent 16-well microfluidic cards, which will be individually activated over the 12 month mission duration. In addition to the wild type and radiation-sensitive mutant strains, a BioSentinel strain of yeast has been developed, which requires a DSB to reactivate growth, thereby allowing for a direct measurement of DSBs caused by radiation. These two missions demonstrate the utility of using autonomous nanosatellites to address strategic knowledge gaps in the push to once again extend manned spaceflight beyond LEO.

Padgen, Mike↗
NASA NTRS1997

Lessons learned studying design issues for lunar and Mars settlements

In a study of lunar and Mars settlement concepts, an analysis was made of fundamental design assumptions in five technical areas against a model list of occupational and environmental health concerns. The technical areas included the proposed science projects to be supported, habitat and construction issues, closed ecosystem issues, the "MMM" issues (mining, material processing, and manufacturing), and the human elements of physiology, behavior, and mission approach. Four major lessons were learned. First it is possible to relate public health concerns to complex technological development in a proactive design mode, which has the potential for long-term cost savings. Second, it became very apparent that prior to committing any nation or international group to spending the billions to start and complete a lunar settlement, over the next century, that a significantly different approach must be taken from those previously proposed, to solve the closed ecosystem and "MMM" problems. Third, it also appears that the health concerns and technology issues to be addressed for human exploration into space are fundamentally those to be solved for human habitation of the Earth (as a closed ecosystem) in the 21st century. Finally, it is proposed that ecosystem design modeling must develop new tools, based on probabilistic models as a step up from closed circuit models.

Life Support Systems↗