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NASA NTRS2014

Evolution of Software-Only-Simulation at NASA IV and V

Software-Only-Simulations have been an emerging but quickly developing field of study throughout NASA. The NASA Independent Verification Validation (IVV) Independent Test Capability (ITC) team has been rapidly building a collection of simulators for a wide range of NASA missions. ITC specializes in full end-to-end simulations that enable developers, VV personnel, and operators to test-as-you-fly. In four years, the team has delivered a wide variety of spacecraft simulations that have ranged from low complexity science missions such as the Global Precipitation Management (GPM) satellite and the Deep Space Climate Observatory (DSCOVR), to the extremely complex missions such as the James Webb Space Telescope (JWST) and Space Launch System (SLS).This paper describes the evolution of ITCs technologies and processes that have been utilized to design, implement, and deploy end-to-end simulation environments for various NASA missions. A comparison of mission simulators are discussed with focus on technology and lessons learned in complexity, hardware modeling, and continuous integration. The paper also describes the methods for executing the missions unmodified flight software binaries (not cross-compiled) for verification and validation activities.

Embedded↗

NASA NTRSDate not supplied

Runtime Monitoring for Unmanned Aerospace Systems with Neural Network Components

AI components (e.g., Deep Neural Networks) are increasingly used in unmanned Aerospace systems for safety-relevant applications. Rigorous Verification and Validation methods for such components are still in their infancy and thus, monitoring of the AI's behavior during runtime is essential. In this paper, we will present a runtime-monitoring architecture, which combines the advanced statistical analysis framework SYSAI (System Analysis using Statistical AI) with temporal and probabilistic runtime monitoring carried out by R2U2 (Realizable, Responsive, and Unobtrusive Unit). Learned statistical models of complex systems with AI components are produced by the SYSAI framework and provide detailed information to enable the R2U2 runtime monitor to efficiently perform advanced safety and performance checks in nominal and off-nominal conditions. We will present initial results of our tool set and architecture on a case study, a DNN-based autonomous centerline tracking system (ACT).

Yuning He↗

NASA NTRS2018

Digital Technologies at NASA for Science and Engineering

While scientific and engineering advancements used to rely primarily on theoretical studies and physical experiments, today digital technology enabled by petaflops-scale supercomputers is an equal, if not a greater, contributor to such achievements. In addition, computational modeling and simulation serves as a predictive tool that is not otherwise available. As a result, the use of high performance computing is integral to NASA's work in all mission areas such as space exploration, aeronautics, and scientific discovery. But traditional supercomputing alone is not sufficient for all of the space agency's needs. The success of many NASA missions depends on solving complex computing challenges, some of which are NP-hard (decision theory) if using classical solution methods. Quantum computing promises an unprecedented ability to solve such intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. Another disruptive digital technology is neuromorphic computing that uses brain-inspired lessons to generate new architectures that are much more energy efficient, and capable of massive parallel processing and learning in-situ. Finally, with large amounts of observational and computational data sets, the opportunities of big data and data analytics can be leveraged to enable deep learning and knowledge discovery - it's all a massive digital transformation. This talk will be an overview how NASA utilizes digital technologies for its science and engineering efforts.

Biswas, Rupak↗

NASA NTRSDate not supplied

Deep Learning-Based Negotiation Strategy Selection for Cooperative Conflict Resolution in Urban Air Mobility

This paper presents a collaborative conflict resolution technique using deep neural network-based intelligent search of the solution space. This approach offers a rapid convergence to a mutually acceptable solution for real-time conflict resolution, suitable for urban air mobility operations. Furthermore, the presented technique allows operational flexibility to the urban air mobility agents where these agents can collaboratively devise the solution via integrative negotiation, based on their local utility functions, as long as such a solution does not violate the global safety thresholds. The presented machine-to-machine negotiation method is built on our prior work on holistic assessment of the airspace and potential conflict detection implemented at-the-edge, onboard the unmanned aircraft systems. This paper extends the prior work to augment decision-making at-the-edge, thereby, promising a true distributed control architecture for urban air mobility. In this approach, each agent (a) builds a potential in-flight conflict map, (b) identifies the conflicting agents, (c) dynamically prepares a list of alternatives based on its current utility functions, (d) negotiates with the conflicting agents to pick one of these alternatives, and (e) implements the negotiated alternative to mutually resolve the conflict. Note that such an approach does not require a contingency plan to be made pre-flight, as the conflict resolution strategies are decided and negotiated in real time based on the present state of the agent. The contingency plan, if available, can serve as an input to the real-time conflict resolution strategy formulation, and also can be used as a fallback plan in case the negotiation fails and the impacted agents need to switch to a rule-based/supervisory resolution mode from the discussed distributed resolution mode. The presented collaborative negotiation-based conflict resolution technique incorporates a time-dependent reward function to catalyze collaborative resolution by incentivizing the agents with local and global rewards beneficial to their business operations.

Advanced Air Mobility↗

NASA NTRS2004

Communicating Medical Needs to Non-Medical Managers

Differences in communication styles and languages between groups often lead to miscommunication, confusion, and/or frustration. Engineers, computer specialists, clinicians, and managers often utilize the English language in very different ways, with different groups using the same words to represent different concepts ("complaint" is a typical example). In addition, medical issues are often perceived as "off-nominal" and not "primary mission tasks" by managers, which can cause them to assign lower priorities to medical training time and resources. Knowledge bases differ due to variations in training and skill sets, and the goals (both immediate and long-term) of the communicators may also vary, with managers being primarily concerned with overall mission objectives, while clinicians focus on individual or group health issues. Furthermore, true communication is only possible when clinicians possess a deep understanding of mission requirements as well as the ability to communicate medical requirements on a priority basis using risk assessment, added value, and cost benefit analysis. These understandable differences may contribute to difficulties in expressing concerns and ideas in an efficient manner, particularly in projects, such as the space program or many military operations, where these varied groups must collaborate, and where the final decisions must be made by fully informed mission commanders. Methods: Three scenario-based approaches were developed utilizing decision trees and problem based learning, to help define and integrate these concepts. Results: Use of these techniques by NASA and military personnel will be presented. Discussion: To enhance communication, particularly of medical needs, one must identify the concerns and motivating factors for the other groups; for example, members of management may focus on financial concerns, a desire for risk mitigation, public perceptions, mission objectives, etc. Training clinicians to frame issues in these terms may lead to better understanding of the medical concerns by other groups.

Bacal, Kira↗

NASA NTRSDate not supplied

From Simulation to Reality With Random Noise

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is notstraight forward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network (ConvNet) to classify position relative to a centerline. Further models are trained with different types of noise progressively added to the data. The models are deployed aboard the on-site test vehicle to test real-world performance. In our experiments, we find that such naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization.

simulation↗

NASA NTRS1996

Experiences in Interagency and International Interfaces for Mission Support

The Flight Dynamics Division (FDD) of the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GFSC) provides extensive support and products for Space Shuttle missions, expendable launch vehicle launches, and routine on-orbit operations for a variety of spacecraft. A major challenge in providing support for these missions is defining and generating the products required for mission support and developing the method by which these products are exchanged between supporting agencies. As interagency and international cooperation has increased in the space community, the FDD customer base has grown and with it the number and variety of external interfaces and product definitions. Currently, the FDD has working interfaces with the NASA Space and Ground Networks, the Johnson Space Center, the White Sands Complex, the Jet propulsion Laboratory (including the Deep Space Network), the United States Air Force, the Centre National d'Etudes Spatiales, the German Spaceflight Operations Center, the European Space Agency, and the National Space Development Agency of Japan. With the increasing spectrum of possible data product definitions and delivery methods, the FDD is using its extensive interagency experience to improve its support of established customers and to provide leadership in adapting/developing new interfaces. This paper describes the evolution of the interfaces between the FDD and its customers, discusses many of the joint activities ith these customers, and summarizes key lessons learned that can be applied to current and future support.

Dell, G. T.↗

NASA NTRS2006

Cassini distributed instrument operations – what we’ve learned since Saturn orbit insertion

The Cassini mission to Saturn is complex with 12 science teams conducting distributed operations across the United States and Europe. Each Team includes scientists from around the world who actively participate in operations, including observation design, instrument commanding, downlink processing, and archiving. This represents a change in how JPL complex deep-space missions have been operated. Since Saturn Orbit Insertion (SOI), the Cassini Project has spent 17 months conducting science operations and has gained realworld experience that has tested the assumptions and rationale for this approach. We have learned that many of the expected benefits have been realized, but there were numerous unexpected challenges as well. This paper will discuss the lessons learned from the Cassini Tour experience to date. It will revisit the assumptions and rationale behind the distributed instrument operations design and will describe the results, good and bad, of implementing this method of operations. We will describe how Instrument Teams are structured, their roles and responsibilities, what challenges they faced going into orbital operations (the “tour”) and what creative solutions were proposed when funding limitations and schedule milestones prevented optimum solutions. We will also discuss the problems that have been encountered both on the ground and with the instruments, how these problems and anomalies were overcome, and what was learned along the way about the characteristics of distributed instrument operations.

Woncik, Pam↗

NASA NTRS2006

Cassini Distributed Instrument Operations: What We've Learned Since Saturn Orbit Insertion

The Cassini mission to Saturn is complex with 12 science teams conducting distributed operations across the United States and Europe. Each Team includes scientists from around the world who actively participate in operations, including observation design, instrument commanding, downlink processing, and archiving. This represents a change in how JPL complex deep-space missions have been operated. Since Saturn Orbit Insertion (SOI), the Cassini Project has spent 17 months conducting science operations and has gained real-world experience that has tested the assumptions and rationale for this approach. We have learned that many of the expected benefits have been realized, but there were numerous unexpected challenges as well. This paper will discuss the lessons learned from the Cassini Tour experience to date. It will revisit the assumptions and rationale behind the distributed instrument operations design and will describe the results, good and bad, of implementing this method of operations. We will describe how Instrument Teams are structured, their roles and responsibilities, what challenges they faced going into orbital operations (the 'tour') and what creative solutions were proposed when funding limitations and schedule milestones prevented optimum solutions. We will also discuss the problems that have been encountered both on the ground and with the instruments, how these problems and anomalies were overcome, and what was learned along the way about the characteristics of distributed instrument operations.

Cassini↗

NASA NTRSDate not supplied

An Open-Science Approach to Address Individual Response to Simulated GCR In Genetically Diverse Populations of Mice and Humans

This project addresses the challenge of understanding and predicting individual radiation sensitivity by integrating genetics, demographics and biomarker characteristics across species (mice and humans). We hypothesize that ex vivo DNA repair response to GCR components is a central determinant of cancer risk from space radiation and can serve as a biomarker of radiation risk in combination with genetics. Automated image quantification of 53BP1+ radiation-induced foci (RIF) during the first 4-48 h post-irradiation was performed as a function of dose and LET in non-immortalized primary skin fibroblasts derived from 76 mice across 15 strains (5 inbred reference strains and 10 collaborative-cross strains) exposed to X rays (0.1, 1 and 4 Gy), 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100sq. μm), as well as in peripheral blood mononuclear cells (PBMCs) from 768 healthy donors (matched ethnicity, 50/50 male/female, 18-70 years old) exposed to gamma rays (0.1 and 1 Gy), 350 MeV/n 28Si, 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100sq. μm). A genome-wide association study (GWAS) was performed on the mouse strains between DNA damage responses to space radiation and single nucleotide polymorphisms (SNPs). We found SNPs, which were significantly associated to the RIF phenotype, mapped to genes and pathways that are functionally linked to health hazards for deep space exploration (e.g. carcinogenesis, nervous system damage and immune dysfunction). Some of these SNPs were located within protein coding regions, potentially interfering with protein functions and providing promising genetic targets for countermeasures. We also found correlations between both spontaneous and radiation-induced DNA damage and SNPs mapped to pathways associated with cellular metabolism. GWAS is undergoing for the human data. All data have been made available via the NASA Space Biology Open-Science database (genelab.nasa.gov) and we will discuss how various genomic and transcriptomic datasets can be accessed for modeling and integrated using machine learning methods for discovering new radiation biology.

Sylvain V Costes↗

NASA NTRS2004

Lunar and Planetary Science XXXV: Astrobiology

The presentations in this session are: 1. A Prototype Life Detection Chip 2. The Geology of Atlantis Basin, Mars, and Its Astrobiological Interest 3. Collecting Bacteria Together with Aerosols in the Martian Atmosphere by the FOELDIX Experimental Instrument Developed with a Nutrient Detector Pattern: Model Measurements of Effectivity 4. 2D and 3D X-ray Imaging of Microorganisms in Meteorites Using Complexity Analysis to Distinguish Field Images of Stromatoloids from Surrounding Rock Matrix in 3.45 Ga Strelley Pool Chert, Western Australia 4. Characterization of Two Isolates from Andean Lakes in Bolivia Short Time Scale Evolution of Microbiolites in Rapidly Receding Altiplanic Lakes: Learning How to Recognize Changing Signatures of Life 5. The Effect of Salts on Electrospray Ionization of Amino Acids in the Negative Mode 6. Determination of Aromatic Ring Number Using Multi-Channel Deep UV Native Fluorescence 7. Microbial D/H Fractionation in Extraterrestrial Materials: Application to Micrometeorites and Mars 8. Carbon Isotope Characteristics of Spring-fed Iron-precipitating Microbial Mats 9. Amino Acid Survival Under Ambient Martian Surface UV Lighting Extraction of Organic Molecules from Terrestrial Material: Quantitative Yields from Heat and Water Extractions 10. Laboratory Detection and Analysis of Organic Compounds in Rocks Using HPLC and XRD Methods 11. Thermal Decomposition of Siderite-Pyrite Assemblages: Implications for Sulfide Mineralogy in Martian Meteorite ALH84001 Carbonate Globules 12. Determination of the Three-Dimensional Morphology of ALH84001 and Biogenic MV-1 Magnetite: Comparison of Results from Electron Tomography and Classical Transmission Electron Microscopy 13. On the Possibility of a Crypto-Biotic Crust on Mars Based on Northern and Southern Ringed Polar Dune Spots 14. Comparative Planetology of the Terrestrial Inner Planets: Implications for Astrobiology 15. A Possible Europa Exobiology 16. A Possible Biogeochemical Model for Titan

Source record↗

NASA NTRS2018

Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.

E L Meszaros↗

NASA NTRSDate not supplied

DeepONet-Assisted Optimization of Surface Topography for Transition Delay in a Mach 4.5 Boundary Layer

We use deep learning, an ensemble variational technique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (DeepONets), which have the known ability to learn complex nonlinear operators within dynamical systems, are used for machine learning. For the baseline configuration of a smooth flat plate, the second-mode waves at the DNS inflow cause a quick nonlinear breakdown of the high-speed boundary layer within the computational domain. Results reported in the present study validate the ability of DeepONets to model the transition delay via a given topography of the roughness element. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substantially lowered by the DeepONets-based reduced-order model. In comparison to the baseline method of EnVar optimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition past the outflow boundary of the computational domain while utilizing almost 5–6 times fewer DNS.

Machine Learning↗

NASA NTRSDate not supplied

DeepONet-Assisted Optimization of Surface Topography for Transition Delay in A Mach 4.5 Boundary Layer

We use deep learning, an ensemble variationaltechnique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (Deep-ONets), which have the known ability to learn com-plex nonlinear operators within dynamical systems,are used for machine learning. For the baseline config-uration of a smooth flat plate, the second-mode wavesat the DNS inflow cause a quick nonlinear breakdownof the high-speed boundary layer within the computa-tional domain. Results reported in the present studyvalidate the ability of DeepONets to model the tran-sition delay via a given topography of the roughnesselement. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substan-tially lowered by the DeepONets-based reduced-ordermodel. In comparison to the baseline method of EnVaroptimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition pastthe outflow boundary of the computational domainwhile utilizing almost 5–6 times fewer DNS.

Machine Learning↗

NASA NTRSDate not supplied

Open-source Techniques for Automated Landslide Inventory Generation for Rapid Response

Manual mapping is the most used method for generating landslide inventories. For rapid response scenario this method becomes tedious and time consuming. The Landslide team at NASA Goddard Space Flight Center has been developing open-source landslide mapping systems for rapid generation of landslide inventories. We have developed a Python-based landslide mapping framework known as the Semi-Automatic Landslide Detection (SALaD) system that uses Object-based Image Analysis and machine learning. For production of event-based inventories, SALaD was modified to include a change detection module (SALaD-CD). Utilizing high-resolution imagery form from Planet and Maxar, we have generated multiple rapid response landslide inventories that have been used by emergency responders on the ground, the NASA Disasters program, and academia. Currently, we are exploiting deep learning frameworks for landslide mapping. We are interested to learn about efficient way to harmonize multi-sensor data for creating a long-term record of landslides, training strategies and ongoing deep learning-based efforts for natural hazard characterization within NASA and UMD.

Pukar Amatya↗

NASA NTRS2020

Convolutional Neural Network for Transition Modeling Based on Linear Stability Theory

Transition prediction is an important aspect of aerodynamic design because of its impact on skin friction and potential coupling with flow separation characteristics. Traditionally, the modeling of transition has relied on correlation-based empirical formulas based on integral quantities such as the shape factor of the boundary layer. However, in many applications of computational fluid dynamics, the shape factor is not straightforwardly available or not well-defined. We propose using the complete velocity profile along with other quantities (e.g., frequency, Reynolds number) to predict the perturbation amplification factor. While this can be achieved with regression models based on a classical fully connected neural network, such a model can be computationally more demanding. We propose a novel convolutional neural network inspired by the underlying physics as described by the stability equations. Specifically, convolutional layers are first used to extract integral quantities from the velocity profiles, and then fully connected layers are used to map the extracted integral quantities, along with frequency and Reynolds number, to the output (amplification ratio). Numerical tests on classical boundary layers clearly demonstrate the merits of the proposed method. More importantly, we demonstrate that, for Tollmien-Schlichting instabilities in two-dimensional, low-speed boundary layers, the proposed network encodes information in the boundary layer profiles into an integral quantity that is strongly correlated to a well-known, physically defined parameter – the shape factor.

Laminar-turbulent transition↗

NASA NTRSDate not supplied

NextSTEP Appendix A Modular ECLSS Effort Lessons Learned

NASA’s Artemis program provides the first steps for earth-independent exploration starting with crewed habitats in cislunar space and progressing toward crewed landings on the lunar surface that will prepare systems and crews for the exploration of Mars. The Next Space Technology for Exploration Partnerships (NextSTEP) is a public-private partnership model that facilitates commercial development of deep space exploration capabilities in support of more extensive human spaceflight missions in and beyond cislunar space. NASA issued the original NextSTEP Broad Agency Announcement (BAA) to U.S. industry in late 2014 and issued the second BAA (NextSTEP-2) in April 2016. The first appendix under NextSTEP-2, Appendix A, focused on developing deep space habitation concepts, engineering design and development, and risk reduction efforts leading to a habitation capability in cislunar space. NASA solicited concepts to develop and refine the evolvable, modular architecture, functional allocation options, standards, and common interfaces required to enable interoperability of the aggregate system to provide long duration deep space transit habitation, specifically enhancements and testing of deep space Environmental Control and Life Support Systems (ECLSS). Collins Aerospace, formerly UTC Aerospace Systems (UTAS), was awarded a Phase 1 and subsequent Phase 2 contract to “develop concepts that group ECLS systems into logical modules maximizing the use of common components and the development of unique methods and design concepts that support in-flight maintenance and repair for future exploration systems.” This paper summarizes the work accomplished under this effort, the lessons that can be applied to development of forthcoming habitation elements, and the gaps remaining to achieve a more resilient, maintainable, repairable and adaptable system capable of installation on a wide variety of habitat platforms. A primary accomplishment of this effort is the development and maturation of a modular palletization concept to enable standard rack interfaces, post-launch outfitting, and decoupling of structural supports that withstand launch environments from those needed for lower on-orbit loads in order to reduce installed mass and repurposing of panels within the habitat. In the course of the effort, Collins assessed numerous architecture trades, including the use of condensing and noncondensing heat exchangers, the ability of modular units to accommodate various habitat volumes and thermal loading, and the most appropriate order of and timing of delivery of regenerative ECLSS hardware to orbital habitats. In addition to the modularity of hardware elements, Collins developed software approaches for distributed/modular command, control, and communication systems and innovative Bayesian fault detection and isolation techniques. Finally, the effort explored advanced maintainability and supportability concepts including the definition of maintenance units (MUs) in place of the traditional Orbital Replacement Units (ORUs), increasing parts commonality to reduce the number and type of spare parts, the use of augmented reality to guide crews during maintenance and repair procedures, and how crews would prepare for and recover from long durations of habitat dormancy. Now that the NextSTEP Modular ECLSS effort has come to a close, it’s important to identify the lessons learned and where they can be leveraged to improve NASA’s broader program of ECLSS technology development and demonstration and ultimately how they can increase the performance of future surface and orbital habitats.

NextSTEP↗

NASA NTRSDate not supplied

NextSTEP Appendix A Modular ECLSS Effort Lessons Learned

NextSTEP↗