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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 685 records · Page 38

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NASA NTRS2014

Preparing for Crew-Control of Surface Robots from Orbit

Since 2010, the European Space Agency (ESA) and the National Aeronautics and Space Administration (NASA) have been developing robots that can be remotely operated on planetary surfaces by astronauts in orbiting spacecraft. A primary objective of this work has been to test telerobotic technologies that are needed for future deep-space human exploration missions. Specifically, ESA's Multi-Purpose End-To-End Robotic Operations Network (METERON) project and NASA's Human Exploration Telerobotics (HET) project are complementary initiatives that aim to validate communications, operations and robotic systems through a range of ground and flight experiments with humans and robots in the loop. Several experiments have already been successfully completed and others are now in preparation for flight.

telerobotics↗

NASA NTRSDate not supplied

Digital Twins and Living Models at NASA

The idea of a “digital twin” was born at NASA in the 1960s as a “living model” of the Apollo mission. In response to Apollo 13’s oxygen tank explosion and subsequent damage to the main engine, NASA employed multiple simulators to evaluate the failure and extended a physical model of the vehicle to include digital components. This “digital twin” was the first of its kind, allowing for a continuous ingestion of data to model the events leading to up to the accident for forensic analysis and exploration of next steps. Fast forward half a century and NASA, along with others in the aerospace community, continues to develop and utilize high-fidelity digital models of physical systems and components as well as the extreme environments in which they operate. NASA aims to travel further and stay longer in space as we realize the Artemis program, taking us from the moon to Mars by establishing a sustainable presence on the Moon to prepare for missions to Mars. We will no longer be able to rely on constant connectivity with an asset nor be in-the-loop for on-demand human intervention in the event of an anomaly. Further, the importance of digital twins is increasing as we seek alternatives for certification of structures so large that they cannot be fully evaluated in existing test facilities and autonomous systems that are not deterministic. The idea of a digital twin is not a new one. What is new is the scale, ordinality, and non-deterministic nature of the models that are critical to achieving NASA’s goals. Their number and autonomy from each other as well as the reference system is suggestive of a changing ecosystem, returning us the idea of a living model.

Digital Twin↗

NASA NTRSDate not supplied

VO 2 -based Thermochromic Variable Emittance Coating

As NASA’s Artemis program requirements become increasingly challenging for thermal control, novel passive thermal control solutions are needed to enable lunar surface and lunar transit operations. A thermochromic coating which changes its heat rejection with temperature could eliminate the need for dual loop thermal control architecture in human spacecraft and could reduce or eliminate the need for survival heaters in robotic spacecraft. The variable heat rejection is accomplished by incorporating a vanadium dioxide (VO 2 ) layer into a multilayer coating stack up. Intrinsically, VO 2 changes from an insulator to a metal at 68 °C, so a tungsten dopant is introduced to reduce the transition temperature to room temperature or below. This project is focused on developing a process for doping the VO 2 to achieve the desired transition temperature

Sydney Taylor↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth independence and autonomy of mission operations. Here we present an overview of AI/ML architecture to support deep space mission goals, developed with leaders in the field. First, we focus on the fundamental biological research that supports our understanding of physiological responses to spaceflight, and we describe current efforts to support AI/ML research including data standardization and data engineering through maximally open and FAIR (findable, accessible, interoperable, reusable) databases and the generation of AI-ready datasets for reuse and analysis. We also discuss remote data management frameworks for research data as well as environmental and health data that are generated during deep space missions. We highlight several research projects that leverage data standardization and management for fundamental biological discovery to uncover the complex effects of space travel on living systems. Next, we provide an overview of cutting-edge AI/ML approaches that can be integrated to support remote monitoring and analysis during deep space missions, including generative models and large language models to learn the underlying biomedical patterns and predict outcomes or answer questions during off world medical scenarios. We also describe current AI/ML methods to support this research and monitoring through automated cloud-based labs which enable limited human intervention and closed-loop experimentation in remote settings. These labs could support mission autonomy by analyzing environmental data streams, and would be facilitated through in situ analytics capabilities to avoid sending large raw data files through low bandwidth communications. Finally, in the context of deep space missions with limited communications or access to medical advice from Earth, we describe a solution for integrated, real-time mission biomonitoring across hierarchical levels from continuous environmental monitoring, to wearables and point-of-care devices, to molecular and physiological monitoring. We introduce a precision space health system that will ensure that the future of space health is predictive, preventative, participatory and personalized.

artificial intelligence↗

NASA NTRS1984

Human operator control of a bilateral teleoperator in part-simulation of zero gravity

Remote manipulators (teleoperators) are defined as mechanical devices which extend human manipulative ability to operational environments which are either hostile to or remote from the human operator. Teleoperation involves the explicit and active inclusion of the human operator (HO) in the system control loop. An investigation of human/teleoperator control interactions in the orbital operational environment has been conducted, taking into account the employment of a model describing human neuromotor control as a linear damped harmonic oscillator. The application of this model for the specification of the end point position in teleoperator control is explored, and two experiments are conducted. The results indicate the potential utility of relatively simple models of neuromotor control processes in investigating the interaction of the human operator and controller in teleoperation.

Corker, K.↗

NASA NTRS2002

Human motion planning based on recursive dynamics and optimal control techniques

This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A quasi-Newton nonlinear programming technique (super-linear convergence) is implemented to solve minimum torque-based human motion-planning problems. The explicit analytical gradients needed in the dynamics are derived using a matrix exponential formulation and Lie algebra. Cubic spline functions are used to make the search space for an optimal solution finite. Based on our formulations, our method is well conditioned and robust, in addition to being computationally efficient. To better illustrate the efficiency of our method, we present results of natural looking and physically correct human motions for a variety of human motion tasks involving open and closed loop kinematic chains.

Non-NASA Center↗

NASA NTRS1975

Kalman filter estimation of human pilot-model parameters

The parameters of a human pilot-model transfer function are estimated by applying the extended Kalman filter to the corresponding retarded differential-difference equations in the time domain. Use of computer-generated data indicates that most of the parameters, including the implicit time delay, may be reasonably estimated in this way. When applied to two sets of experimental data obtained from a closed-loop tracking task performed by a human, the Kalman filter generated diverging residuals for one of the measurement types, apparently because of model assumption errors. Application of a modified adaptive technique was found to overcome the divergence and to produce reasonable estimates of most of the parameters.

Schiess, J. R.↗

NASA NTRS2010

Ultra Reliable Closed Loop Life Support for Long Space Missions

Spacecraft human life support systems can achieve ultra reliability by providing sufficient spares to replace all failed components. The additional mass of spares for ultra reliability is approximately equal to the original system mass, provided that the original system reliability is not too low. Acceptable reliability can be achieved for the Space Shuttle and Space Station by preventive maintenance and by replacing failed units. However, on-demand maintenance and repair requires a logistics supply chain in place to provide the needed spares. In contrast, a Mars or other long space mission must take along all the needed spares, since resupply is not possible. Long missions must achieve ultra reliability, a very low failure rate per hour, since they will take years rather than weeks and cannot be cut short if a failure occurs. Also, distant missions have a much higher mass launch cost per kilogram than near-Earth missions. Achieving ultra reliable spacecraft life support systems with acceptable mass will require a well-planned and extensive development effort. Analysis must determine the reliability requirement and allocate it to subsystems and components. Ultra reliability requires reducing the intrinsic failure causes, providing spares to replace failed components and having "graceful" failure modes. Technologies, components, and materials must be selected and designed for high reliability. Long duration testing is needed to confirm very low failure rates. Systems design should segregate the failure causes in the smallest, most easily replaceable parts. The system must be designed, developed, integrated, and tested with system reliability in mind. Maintenance and reparability of failed units must not add to the probability of failure. The overall system must be tested sufficiently to identify any design errors. A program to develop ultra reliable space life support systems with acceptable mass should start soon since it must be a long term effort.

Jones, Harry W.↗

DOE OSTI2024

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Understanding the interaction between humans and the Earth system is a computationally daunting task, with many possible approaches depending on resources available and questions of interest. For example, state-of-the-art impact models require decade-long time series of relatively high frequency, spatially resolved and often multiple variables representing climatic impact-drivers (Ruane et al., 2022). Most commonly these are derived from the outputs of detailed, computationally expensive Earth System Models (ESMs) run according to a standard, limited set of future scenarios, the latest being the SSP-RCPs run under CMIP6/ScenarioMIP (Eyring et al., 2016; O’Neill et al., 2016). At the time of writing, O’Neill et al. (2016) has been cited more than 1750 times and Eyring et al. (2016) more than 5000 times, highlighting the broad, general applications of this data. Often, however, impact modeling seeks to explore new scenarios that were not part of the ScenarioMIP protocol, and/or needs a larger set of initial condition ensemble members than are typically available to quantify the effects of ESM internal variability. In addition, the recognition that the human and Earth systems are fundamentally intertwined, and may feature potentially significant feedback loops, is making integrated, simultaneous modeling of the coupled human-Earth system increasingly necessary, if computationally challenging with most existing tools (Thornton et al., 2017). For impact modelers, climate model emulators can be the answer to meet both the needs of: 1) creating realizations for novel scenarios and 2) achieving a simplified, computationally tractable representation of ESM behavior in a coupled human-Earth system modeling framework. We proposed a new, comprehensive approach to such emulation of gridded, multivariate ESM outputs for novel scenarios without the computational cost of a full ESM, STITCHES (Tebaldi et al., 2022). The approach outlined in Tebaldi et al. (2022) should be extensible to future CMIP eras, although the STITCHES software at present is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/). The corresponding STITCHES Python package uses existing archives of ESMs’ scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate realizations of new scenarios provided by reduced complexity climate models (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018), or to enrich existing initial condition ensembles. Its output provides the same characteristics as the emulated ESM output: multivariate (spanning potentially all variables that the ESM has saved), spatially resolved (down to the native grid of the ESM), and preserving the same high frequency as the original data. A new realization of multiple variables can be generated on the order of minutes with STITCHES, rather than the hours or sometimes days that ESMs require.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2010

Space Suit Environment Testing of the Orion Atmosphere Revitalization Technology

An amine-based carbon dioxide (CO2) and water vapor sorbent in pressure-swing regenerable beds has been developed by Hamilton Sundstrand and baselined for the Orion Atmosphere Revitalization System (ARS). In three previous years at this conference, reports were presented on extensive Johnson Space Center (JSC) testing of this technology. That testing was performed in a sea-level pressure environment with both simulated and real human metabolic loads, and in both open and closed-loop configurations. The Orion ARS is designed to also support space-suited operations in a depressurized cabin, so the next step in developmental testing at JSC was to test the ARS technology in a typical closed space suit-loop environment with low-pressure oxygen inside the process loop and vacuum outside the loop. This was the first instance of low-pressure, high-oxygen, closed-loop testing of the Orion ARS technology, and it was conducted with simulated human metabolic loads in March 2009. The test investigated pressure drops and flow balancing through two different styles of prototype suit umbilical connectors. General swing-bed performance was tested with both umbilical configurations, as well as with a short jumper line installed in place of the umbilicals. Other interesting results include observations on the thermal effects of swing-bed operation in a vacuum environment and a recommendation of cycle time to maintain acceptable suit atmospheric CO2 and moisture levels.

Button, Amy B.↗

NASA NTRS2012

Nanoporous Materials in Atmosphere Revitalization

Atmospheric Revitalization (AR) is the term the National Aeronautics and Space Administration (NASA) uses to encompass the engineered systems that maintain a safe, breathable gaseous atmosphere inside a habitable space cabin. An AR subsystem is a key part of the Environmental Control and Life Support (ECLS) system for habitable space cabins. The ultimate goal for AR subsystem designers is to 'close the loop', that is, to capture gaseous human metabolic products, specifically water vapor (H2O) and Carbon dioxide (CO2), for maximal Oxygen (o2) recovery and to make other useful resources from these products. The AR subsystem also removes trace chemical contaminants from the cabin atmosphere to preserve cabin atmospheric quality, provides O2 and may include instrumentation to monitor cabin atmospheric quality. Long duration crewed space exploration missions require advancements in AR process technologies in order to reduce power consumption and mass and to increase reliability compared to those used for shorter duration missions that are typically limited to Low Earth Orbit. For example, current AR subsystems include separate processors and process air flow loops for removing metabolic CO2 and volatile organic tract contaminants (TCs). Physical adsorbents contained in fixed, packed beds are employed in these processors. Still, isolated pockets of high carbon dioxide have been suggested as a trigger for crew headaches and concern persists about future cabin ammonia (NH3) levels as compared with historical flights. Developers are already focused on certain potential advancements. ECLS systems engineers envision improving the AR subsystem by combining the functions of TC control and CO2 removal into a single regenerable process and moving toward structured sorbents - monoliths - instead of granular material. Monoliths present a lower pressure drop and eliminate particle attrition problems that result from bed containment. New materials and configurations offer promise for lowering cabin levels of CO2 and NH3 as well as reducing power requirements and increasing reliability. This chapter summarizes the challenges faced by ECLS system engineers in pursuing these goals, and the promising materials developments that may be part of the technical solution for challenges of crewed space exploration beyond LEO.

Hernandez-Maldonado, J.↗

NASA NTRS1990

Telefunctioning - An approach to telerobotic manipulations

This study introduces three concepts: telefunctioning, a control method for achieving telefunctioning, and an approach to the analysis of human-robot interaction when telefunctioning governs the system behavior. Telefunctioning facilitates maneuvering of loads by creating a perpetual sense of the load dynamics for the operator. Telefunctioning is defined as a robotic manipulation method in which the dynamic behaviors of the slave robot and the master robot are functions of each other; these functions are the designer's choice and depend on the application. In a subclass of telefunctioning called telepresence, all of the relationships between the master and the slave are specified as unity so that all of the master and slave variables (e.g., position and velocity) are dynamically equal. To create telefunctioning, a minimum number of functions relating the robots' variables is identified. A control architecture which guarantees that the defined functions govern the dynamic behavior of the system is then developed. The stability of the closed-loop system (master robot, slave robot, human, and the load being manipulated) is analyzed, and sufficient conditions for stability are derived.

Kazerooni, H.↗

NASA NTRS2012

Using Pyrolysis and its Bioproducts to Help Close the Loop in Sustainable Life Support Systems

The next step in human exploration of space is beyond low Earth orbit and possibly to sites such as the Moon and Mars. Resupply of critical life support components for missions such as these are difficult or impossible. Life support processes for closing the loop of water, oxygen and carbon have to be identified .. Currently, there are many technologies proposed for terrestrial missions for waste, water, air processing and the creation of consumables. There are a variety of different approaches, but few address all of these issues simultaneously. One candidate is pyrolysis; a method where waste streams can be heated in the absence of oxygen to undergo a thermochemical conversion producing a series of bioproducts. Bioproducts like biochar made from non-edible biomass and human solid waste can possibly provide valuable benefits such as waste reduction, regolith fertilization for increased food production, and become a consumable for water processing and air revitalization systems. Syngas containing hydrogen, carbon monoxide and c~bon dioxide, can be converted to methane and dimethyl ether to create propellants. Bio-oils can be utilized as a heating fuel or fed to bioreactors that utilize oil-eating microbes. Issues such as carbon sequestration and subsequent carbon balance of the closed system and identifying ideal process methods to achieve the highest quality products, whilst being energy friendly, will also be addressed.

McCoy, LaShelle E.↗

NASA NTRS2013

A Review: Using Pyrolysis and its Bioproducts to Help Close the Loop in Sustainable Life Support Systems

The next step in human exploration of space is beyond low Earth orbit and possibly to sites such as the Moon and Mars. Resupply of critical life support components for missions such as these are difficult or impossible. Life support processes for closing the loop of water, oxygen and carbon have to be identified. Currently, there are many technologies proposed for terrestrial missions for waste, water, air processing. and the creation of consumables. There are a variety of different approaches, but few address all of these issues simultaneously. One candidate is pyrolysis; a method where waste streams can be heated in the absence of oxygen to undergo a thermochemical conversion producing a series of bioproducts. Bioproducts like biochar made from non-edible biomass and human solid waste can possibly provide valuable benefits such as waste reduction, regolith fertilization for increased food production, and become a consumable for water processing and air revitalization systems. Syngas containing hydrogen, carbon monoxide and carbon dioxide, can be converted to methane and dimethyl ether to create propellants. Bio-oils can be utilized as a heating fuel or fed to bioreactors that utilize oil-eating microbes.

McCoy, LaShelle E.↗

DOE OSTI2021

Models and Processes to Extract Drug-like Molecules From Natural Language Text

Researchers worldwide are seeking to repurpose existing drugs or discover new drugs to counter the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). A promising source of candidates for such studies is molecules that have been reported in the scientific literature to be drug-like in the context of viral research. However, this literature is too large for human review and features unusual vocabularies for which existing named entity recognition (NER) models are ineffective. We report here on a project that leverages both human and artificial intelligence to detect references to such molecules in free text. We present 1) a iterative model-in-the-loop method that makes judicious use of scarce human expertise in generating training data for a NER model, and 2) the application and evaluation of this method to the problem of identifying drug-like molecules in the COVID-19 Open Research Dataset Challenge (CORD-19) corpus of 198,875 papers. We show that by repeatedly presenting human labelers only with samples for which an evolving NER model is uncertain, our human-machine hybrid pipeline requires only modest amounts of non-expert human labeling time (tens of hours to label 1778 samples) to generate an NER model with an F-1 score of 80.5%—on par with that of non-expert humans—and when applied to CORD’19, identifies 10,912 putative drug-like molecules. This enriched the computational screening team’s targets by 3,591 molecules, of which 18 ranked in the top 0.1% of all 6.6 million molecules screened for docking against the 3CLPro protein.

60 APPLIED LIFE SCIENCES↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRS2016

NASA Advanced Explorations Systems: Advancements in Life Support Systems

The NASA Advanced Exploration Systems (AES) Life Support Systems (LSS) project strives to develop reliable, energy-efficient, and low-mass spacecraft systems to provide environmental control and life support systems (ECLSS) critical to enabling long duration human missions beyond low Earth orbit (LEO). Highly reliable, closed-loop life support systems are among the capabilities required for the longer duration human space exploration missions assessed by NASA's Habitability Architecture Team (HAT). The LSS project is focused on four areas: architecture and systems engineering for life support systems, environmental monitoring, air revitalization, and wastewater processing and water management. Starting with the international space station (ISS) LSS systems as a point of departure (where applicable), the mission of the LSS project is three-fold: 1. Address discrete LSS technology gaps 2. Improve the reliability of LSS systems 3. Advance LSS systems towards integrated testing on the ISS. This paper summarized the work being done in the four areas listed above to meet these objectives. Details will be given on the following focus areas: Systems Engineering and Architecture- With so many complex systems comprising life support in space, it is important to understand the overall system requirements to define life support system architectures for different space mission classes, ensure that all the components integrate well together and verify that testing is as representative of destination environments as possible. Environmental Monitoring- In an enclosed spacecraft that is constantly operating complex machinery for its own basic functionality as well as science experiments and technology demonstrations, it's possible for the environment to become compromised. While current environmental monitors aboard the ISS will alert crew members and mission control if there is an emergency, long-duration environmental monitoring cannot be done in-orbit as current methodologies rely largely on sending environmental samples back to Earth. The LSS project is developing onboard analysis capabilities that will replace the need to return air and water samples from space for ground analysis. Air Revitalization- The air revitalization task is comprised of work in carbon dioxide removal, oxygen generation and recovery and trace contamination and particulate control. The CO2 Removal and associated air drying development efforts under the LSS project are focused both on improving the current SOA technology on the ISS and assessing and examining the viability of other sorbents and technologies available in academia and industry. The Oxygen Generation and Recovery technology development area encompasses several sub-tasks in an effort to supply O2 to the crew at the required conditions, to recover O2 from metabolic CO2, and to recycle recovered O2 back to the cabin environment. Current state-of-the-art oxygen generation systems aboard space station are capable of generating or recovering approximately 40% of required oxygen; for exploration missions this percentage needs to be greatly increased. A spacecraft cabin trace contaminant and particulate control system serves to keep the environment below the spacecraft maximum allowable concentration (SMAC) for chemicals and particulates. Both passive (filters) and active (scrubbers) methods contribute to the overall TC & PC design. Work in the area of trace contamination and particulate control under the LSS project is focused on making improvements to the SOA TC & PC systems on ISS to improve performance and reduce consumables. Wastewater Processing and Water Management- A major goal of the LSS project is the development of water recovery systems to support long duration human exploration beyond LEO. Current space station wastewater processing and water management systems distill urine and wastewater to recover water from urine and humidity condensate in the spacecraft at a approximately 74% recovery rate. For longer, farther missions into deep space, that recovery rate must be greatly increased so that astronauts can journey for months without resupply cargo ships from Earth.

Shull, Sarah A.↗

NASA NTRS2016

NASA Advanced Exploration Systems: Advancements in Life Support Systems

Shull, Sarah A.↗