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NASA NTRS2014

Current Pressure Transducer Application of Model-based Prognostics Using Steady State Conditions

Prognostics is the process of predicting a system's future states, health degradation/wear, and remaining useful life (RUL). This information plays an important role in preventing failure, reducing downtime, scheduling maintenance, and improving system utility. Prognostics relies heavily on wear estimation. In some components, the sensors used to estimate wear may not be fast enough to capture brief transient states that are indicative of wear. For this reason it is beneficial to be capable of detecting and estimating the extent of component wear using steady-state measurements. This paper details a method for estimating component wear using steady-state measurements, describes how this is used to predict future states, and presents a case study of a current/pressure (I/P) Transducer. I/P Transducer nominal and off-nominal behaviors are characterized using a physics-based model, and validated against expected and observed component behavior. This model is used to map observed steady-state responses to corresponding fault parameter values in the form of a lookup table. This method was chosen because of its fast, efficient nature, and its ability to be applied to both linear and non-linear systems. Using measurements of the steady state output, and the lookup table, wear is estimated. A regression is used to estimate the wear propagation parameter and characterize the damage progression function, which are used to predict future states and the remaining useful life of the system.

Fault Estimation↗

NASA NTRS2007

U-937 Toxicity Testing of Lunar Dust Stimulant (JSC-1A-vf)

With NASA planning to extend the human presence to the moon by 2020, the dangers of the lunar environment must be assessed and appropriate countermeasures must be developed. Possible toxic effects of the lunar dust are of particular importance to human health because of the dust's chemical composition, reactivity, and small size. This project focuses on the toxicity of lunar dust stimulant (JSC-1A-vf), in both its active and passive forms, using U-937 human monocyte cells. Simulant was mechanically activated from its passive form by grinding, and its ability to produce hydroxyl radicals was determined. To test for toxicity, active and passivated simulant was diluted in media and applied to the cells for various time periods. Toxicity was then estimated using flow cytometry on the Guava Personal Cell Analysis system. Preliminary results suggest that passivated stimulant is slightly toxic, with an increase in toxicity for activated stimulant. Toxicity results may be affected by cell lysing behavior and quenching of hydroxyl radical production by the cell media.

Bales, Kristyn↗

NASA NTRS1992

Lab-scale Lidar Sensing of Diesel Engines Exhausts

Combustion technology and its environmental concerns are being considered with increasing attention, not only for global-scale effects, but also for toxicological implications, particularly in the lift conditions of traffic-congested areas and industrial sites. Majority combustion by-products (CO, NO(sub x)) and unburned hydrocarbons (HC), are already subject to increasingly severe regulations; however other, non-regulated minority species, mainly soot and heavy aromatic molecules, involve higher health risks, as they are suspected to be agents of serious pathologies and even mutagenic effects. This is but one of the reasons why much research work is being carried out worldwide on the physical properties of these substances. Correspondingly, the need arises to detect their presence in urban environments, with as high a sensitivity as is required by their low concentrations, proper time- and space-resolutions, and 'real-time' capabilities. Lidar techniques are excellent candidates to this purpose, although severe constraints limit their applicability, eye-safety problems and aerosol Mie scattering uncertainties above all. At CNR's Istituto Motori in Napels, a Lidar-like diagnostic system is being developed, aimed primarily at monitoring the dynamic behavior of internal combustion engines, particularly diesel exhausts, and at exploring the feasibility of a so-called 'Downtown Lidar'.

Borghese, A.↗

NASA NTRS2009

Recent Advances in Thermoplastic Puncture-Healing Polymers

Self-healing materials provide a route for enhanced damage tolerance in materials for aerospace applications. In particular, puncture-healing upon impact has the potential to mitigate significant damage caused by high velocity micrometeoroid impacts. This type of material also has the potential to improve damage tolerance in load bearing structures to enhance vehicle health and aircraft durability. The materials being studied are those capable of instantaneous puncture healing, providing a mechanism for mechanical property retention in lightweight structures. These systems have demonstrated healing capability following penetration of fast moving projectiles -- velocities that range from 9 mm bullets shot from a gun (approx.330 m/sec) to close to micrometeoroid debris velocities of 4800 m/sec. In this presentation, we report on a suite of polymeric materials possessing this characteristic. Figure 1 illustrates the puncture healing concept. Puncture healing in these materials is dependent upon how the combination of a polymer's viscoelastic properties responds to the energy input resulting from the puncture event. Projectile penetration increases the temperature in the vicinity of the impact. Self-healing behavior occurs following puncture, whereby energy must be transferred to the material during impact both elastically and inelastically, thus establishing two requirements for puncture healing to occur: a.) The need for the puncture event to produce a local melt state in the polymer material and b.) The molten material has to have sufficient melt elasticity to snap back and close the hole. 1,2 Previous ballistic testing studies revealed that Surlyn materials warmed up to a temperature approx.98 C during projectile puncture (3 C higher than it s melting temperature). 1,2 The temperature increase produces a localized flow state and the melt elasticity to snap back thus sealing the hole. Table 1 lists the commercially polymers studied here, together with their physical properties. The polymers were selected based on chemical structure, tensile strengths, tensile moduli, glass transition temperature, melting temperatures, and impact strength. The thermal properties of the polymers were characterized by Differential Scanning Calorimetry (DSC) and Dynamic Mechanical Analysis (DMA). Mechanical properties were assessed by a Sintech 2W instron according to ASTM D1708 or D638 at crosshead speeds of 5.08 cm/min. 7.6 cm x 7.6 cm panels of the different materials were prepared and ballistic testing was performed at various temperatures. The panels were shot with a .223 caliber semiautomatic rifle from a distance of 23 meters at various temperatures. Chronographs were used to measure initial and final bullet velocity. Temperatures at the site of impact were measured using a FLIR ThermaCAM S60 thermal camera. A Vision Research model Phantom 9 high speed video camera was used to capture high speed video footage of ballistics testing.

Gordon, K. L.↗

NASA NTRS1998

X-33/RLV System Health Management/Vehicle Health Management

To reduce operations costs, Reusable Launch Vehicles (RLVS) must include highly reliable robust subsystems which are designed for simple repair access with a simplified servicing infrastructure, and which incorporate expedited decision-making about faults and anomalies. A key component for the Single Stage To Orbit (SSTO) RLV system used to meet these objectives is System Health Management (SHM). SHM incorporates Vehicle Health Management (VHM), ground processing associated with the vehicle fleet (GVHM), and Ground Infrastructure Health Management (GIHM). The primary objective of SHM is to provide an automated and paperless health decision, maintenance, and logistics system. Sanders, a Lockheed Martin Company, is leading the design, development, and integration of the SHM system for RLV and for X-33 (a sub-scale, sub-orbit Advanced Technology Demonstrator). Many critical technologies are necessary to make SHM (and more specifically VHM) practical, reliable, and cost effective. This paper will present the X-33 SHM design which forms the baseline for the RLV SHM, and it will discuss applications of advanced technologies to future RLVs. In addition, this paper will describe a Virtual Design Environment (VDE) which is being developed for RLV. This VDE will allow for system design engineering, as well as program management teams, to accurately and efficiently evaluate system designs, analyze the behavior of current systems, and predict the feasibility of making smooth and cost-efficient transitions from older technologies to newer ones. The RLV SHM design methodology will reduce program costs, decrease total program life-cycle time, and ultimately increase mission success.

Mouyos, William↗

NASA NTRS2003

Characterization of Model-Based Reasoning Strategies for Use in IVHM Architectures

Open architectures are gaining popularity for Integrated Vehicle Health Management (IVHM) applications due to the diversity of subsystem health monitoring strategies in use and the need to integrate a variety of techniques at the system health management level. The basic concept of an open architecture suggests that whatever monitoring or reasoning strategy a subsystem wishes to deploy, the system architecture will support the needs of that subsystem and will be capable of transmitting subsystem health status across subsystem boundaries and up to the system level for system-wide fault identification and diagnosis. There is a need to understand the capabilities of various reasoning engines and how they, coupled with intelligent monitoring techniques, can support fault detection and system level fault management. Researchers in IVHM at NASA Ames Research Center are supporting the development of an IVHM system for liquefying-fuel hybrid rockets. In the initial stage of this project, a few readily available reasoning engines were studied to assess candidate technologies for application in next generation launch systems. Three tools representing the spectrum of model-based reasoning approaches, from a quantitative simulation based approach to a graph-based fault propagation technique, were applied to model the behavior of the Hybrid Combustion Facility testbed at Ames. This paper summarizes the characterization of the modeling process for each of the techniques.

Poll, Scott↗

NASA NTRS2017

Strain Monitoring of Flexible Structures

One of the biggest challenges facing NASA's deep space exploration goals is structural mass. A long duration transit vehicle on a journey to Mars, for example, requires a large internal volume for cargo, supplies and crew support. As with all space structures, a large pressure vessel is not enough. The vehicle also requires thermal, micro-meteoroid, and radiation protection, a navigation and control system, a propulsion system, and a power system, etc. As vehicles get larger, their associated systems also get larger and more complex. These vehicles require larger lift capacities and force the mission to become extremely costly. In order to build large volume habitable vehicles, with only minimal increases in launch volume and mass, NASA is developing lightweight structures. Lightweight structures are made from non-metallic materials including graphite composites and high strength fabrics and could provide similar or better structural capability than metals, but with significant launch volume and mass savings. Fabric structures specifically, have been worked by NASA off and on since its inception, but most notably in the 1990's with the TransHAB program. These TransHAB developed structures use a layered material approach to form a pressure vessel with integrated thermal and micro-meteoroid and orbital debris (MMOD) protection. The flexible fabrics allow the vessel to be packed in a small volume during launch and expand into a much larger volume once in orbit. NASA and Bigelow Aerospace recently installed the first human-rated inflatable module on the International Space Station (ISS), known as the Bigelow Expandable Activity Module (BEAM) in May of 2016. The module provides a similar internal volume to that of an Orbital ATK Cygnus cargo vehicle, but with a 77% launch volume savings. As lightweight structures are developed, testing methods are vital to understanding their behavior and validating analytical models. Common techniques can be applied to fabric materials, such as tensile testing, fatigue testing, and shear testing, but common measurement techniques cannot be used on fabric. Measuring strain in a material and during a test is a critical parameter for an engineer to monitor the structure during the test and correlate to an analytical model. The ability to measure strain in fabric structures is a challenge for NASA. Foil strain gauges, for example, are commonplace on metallic structures testing, but are extremely difficult to interface with a fabric substrate. New strain measuring techniques need to be developed for use with fabric structures. This paper investigates options for measuring strain in fabric structures for both ground testing and in-space structural health monitoring. It evaluates current commercially available options and outlines development work underway to build custom measurement solutions for NASA's fabric structures.

Litteken, Douglas A.↗

NASA NTRSDate not supplied

Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders

Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.

quantum machine learning↗

NASA NTRS2016

Development and Provision of Functional Foods to Promote Health on Long-Duration Space Missions

During long-duration NASA space missions, such as proposed missions to Mars, astronauts may experience negative physiological effects such as bone loss. Functional foods such as high-lycopene, high-flavonoids and high-omega-3 products and fruits and vegetables may mitigate the negative effects of spaceflight on physiological factors including the bone health of crewmembers. Previous studies showed that current ISS provisions provide high-lycopene and high-omega-3 food items but the variety is limited, which could promote menu fatigue. Bioactive compounds can degrade like other chemical compounds and lose functionality. The native concentrations and stability of bioactive compounds have never been determined in spaceflight foods, and adequate information is not available for commercial products for the storage durations required for space exploration (5 years). The purpose of this task is to develop new spaceflight foods that are high in omega-3 fatty acids, lycopene, or flavonoids, identify commercial products with these bioactive compounds that meet spaceflight requirements, and define the stability of these nutrients in storage to enable purposeful functional food incorporation into the space food system. The impact of storage temperature on the stability of lutein, lycopene, beta-carotene, omega-3 fatty acids, phenolics, anthocyanins and sterols is being studied in 12 ISS menu items stored at three different temperatures (4, 21, 35 degree C) over 2 years. Additionally, nutrient and quality stability are being assessed on a larger food set stored at 21 degree C over 2 years that contains twelve newly developed foods, 10 commercial products repackaged to spaceflight requirements, and another 5 current ISS menu items expected to be good sources of omega-3 fatty acids, lycopene, or flavonoids. All items were shipped overnight to the Linus Pauling Institute at Oregon State University (Corvalis, OR) after processing and 1-year of storage and analyzed for bioactive compound concentrations. Sensory evaluation was conducted on the newly developed functional foods and commercial products with untrained panelists (n is greater than or equal to 25) using a 9-point Hedonic scale to test sensory attributes and overall acceptability after processing and 1-year of storage (21 degree C). Repeat nutritional and sensory analyses will be conducted in the same foods after the 2-year storage period is completed. The stability of bioactive compounds in the selected foods was dependent on storage temperature and food matrix. Omega-3 showed excellent stability in the analyzed products after 1-year of storage, regardless of the storage temperature; phenolic compounds also showed good stability. Lycopene was more stable in oil-based products rather than water-based products because of the protection that lipids offer to lycopene molecules. Also, lycopene was more stable in freeze-dried products than in high moisture foods. The 12 newly developed functional foods showed good overall acceptability in sensory attributes after processing (average score 7.2 out of 9.0) and maintained sensory quality through 1-year (21 degree C); the overall acceptability was on average 7.1 after storage. Similar behavior was observed for the 10 commercial products after 1 year. The developed products are good sources of omega-3 (both plant and marine), vegetables (7 vegetable-based products), and good sources of carotenoids, such as the Curry Pumpkin Soup and the Sweet and Savory Kale. Nine of the new products, such as Mango Salad, Pickled Beets, and Braised Red Cabbage, are rich in phenolic compounds. Stability of most of the studied nutrients seems to be adequate after 1-year of storage in most of the tested foods. However, storage temperature of the food must be considered during long-duration space missions to achieve stability of all nutrients. Likewise, more information is needed regarding nutrient retention after 2-years of storage to identify nutritional gaps that may be expected over the 5-year shelf life required for a Mars mission. New developed products will be filling a gap in the current space food system to minimize menu fatigue, provide specific nutrients to reduce the negative effects of long-duration space missions and maintain crew members' health. Information about bioactive compounds in developed products after 1-year and 2-year of storage will provide the knowledge base for further product development.

Bermudez-Aguirre, D.↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

open access↗

NASA NTRS2022

Automating Surface Attitude Positioning and Pointing Operations for Mars 2020

The Surface Attitude Positioning and Pointing (SAPP) subsystem of the Mars Perseverance rover keeps track of the rover’s position and attitude on the surface of Mars. The SAPP Downlink Engineering Operations team members receive data from the rover on a daily basis. They must interpret the data to make sure the rover is staying safe and to support uplink planning. The SAPP team keeps track of the error growth in the rover’s attitude estimate due to noise in the Rover Inertial Measurement Unit’s (RIMU) gyroscopes used to propagate that attitude estimate whenever the rover is moving. Whenever this error grows to a particular threshold, SAPP is responsible for updating the onboard attitude knowledge using the RIMU’s accelerometers to estimate rover roll and pitch and sun imaging to estimate rover yaw, thereby reducing this attitude estimation error. Accurate attitude estimation is required so that the rover can successfully point its High Gain Antenna (HGA) to receive information from Earth and as a backup to the Mars orbiters used for sending data from the rover to Earth, point instruments on its Remote Sensing Mast (RSM), and support safe movement and placement of instruments by the rover’s ARM relative to the Martian surface. The Mars 2020 Engineering Operations team has been working to increase the operational efficiency of the mission and eventually move to a five-hour timeline for daily operations. In pursuit of this goal, the SAPP Engineering Operations team has automated their downlink process by developing a centralized Jupyter notebook to analyze the data received daily from the rover. The SAPP downlink Jupyter notebook automatically collects the data relevant to the SAPP subsystem and visualizes this information in plots and tables that can be easily read by downlink operators to aid them in assessing the status of the subsystem. Various Application Programming Interfaces (APIs) have been incorporated into the downlink daily notebook to automate the collection and posting of data, such as gathering and posting data products to the cloud. The SAPP team has also developed a SAPP downlink software library that includes functions to aid the notebook in processing data. In addition to assessing the SAPP subsystem on a daily basis, operators need to assess the long-term trending behavior of the subsystem over time. An automated trending process has been developed to collect information from the daily notebooks in order to plot and analyze that data in a centralized place. These daily and trending processes have expedited the SAPP downlink assessment and laid the groundwork to completely automate the SAPP downlink process so that SAPP operators are unnecessary unless something unexpected occurs. This paper will provide an overview of the functions that the SAPP subsystem carries out on a daily basis, and will then dive into the automations that have been developed for daily and trending downlink assessment. An assessment of the downlink efficiency will be provided, along with a summary of lessons learned and work to go. Finally, the authors will discuss how these types of automated spacecraft health assessments could be more broadly used within mission operations.

Zarifian, Anais↗

NASA NTRS2007

Multi-System Effects of Daily Artificial Gravity Exposures in Humans Deconditioned by Bed Rest

We have begun to explore the utility of intermittent artificial gravity (AG) as a multi-system countermeasure to the untoward health and performance effects of adaptation to decreased gravity during prolonged space flight. The first study in this exploration was jointly designed by an international, multi-disciplinary team of scientists interested in standardizing an approach so that comparable data could be obtained from follow-on studies performed in multiple international locations. Fifteen rigorously screened male volunteers participated in the study after providing written informed consent. All were subjected to 21 days of 6deg head-down-tilt (HDT) bed rest. Eight were treated with daily 1hr AG exposures (2.5g at the feet decreasing to 1.0g at the heart) aboard a short radius (3m) centrifuge, while the other seven served as controls. Multiple observations were made of dependent measures in the bone, muscle, cardiovascular, sensory-motor, immune, and behavioral systems during a 10 day acclimatization period prior to HDT bed rest and again during an 8 day recovery period after the bed rest period. Comparisons between the treatment and control subjects demonstrated salutary effects of the AG exposure on aspects of the muscle and cardiovascular systems, with no untoward effects on the vestibular system, the immune system, or cognitive function. Bone deconditioning was similar between the treatment and control groups, suggesting that the loading provided by this specific AG paradigm was insufficient to protect that system from deconditioning. Future work will be devoted to varying the loading duty cycle and/or coupling the AG loading with exercise to provide maximum physiological protection across all systems. Testing will also be extended to female subjects. The results of this study suggest that intermittent AG could be an effective multi-system countermeasure.

Paloski, William H.↗

NASA NTRS2016

A Generic Modeling Process to Support Functional Fault Model Development

Functional fault models (FFMs) are qualitative representations of a system's failure space that are used to provide a diagnostic of the modeled system. An FFM simulates the failure effect propagation paths within a system between failure modes and observation points. These models contain a significant amount of information about the system including the design, operation and off nominal behavior. The development and verification of the models can be costly in both time and resources. In addition, models depicting similar components can be distinct, both in appearance and function, when created individually, because there are numerous ways of representing the failure space within each component. Generic application of FFMs has the advantages of software code reuse: reduction of time and resources in both development and verification, and a standard set of component models from which future system models can be generated with common appearance and diagnostic performance. This paper outlines the motivation to develop a generic modeling process for FFMs at the component level and the effort to implement that process through modeling conventions and a software tool. The implementation of this generic modeling process within a fault isolation demonstration for NASA's Advanced Ground System Maintenance (AGSM) Integrated Health Management (IHM) project is presented and the impact discussed.

Design Analysis↗

NASA NTRS2017

Systems Engineering for Space Exploration Medical Capabilities

Human exploration missions that reach destinations beyond low Earth orbit, such as Mars, will present significant new challenges to crew health management. For the medical system, lack of consumable resupply, evacuation opportunities, and real-time ground support are key drivers toward greater autonomy. Recognition of the limited mission and vehicle resources available to carry out exploration missions motivates the Exploration Medical Capability (ExMC) Element's approach to enabling the necessary autonomy. The Element's work must integrate with the overall exploration mission and vehicle design efforts to successfully provide exploration medical capabilities. ExMC is applying systems engineering principles and practices to accomplish its goals. This paper discusses the structured and integrative approach that is guiding the medical system technical development. Assumptions for the required levels of care on exploration missions, medical system goals, and a Concept of Operations are early products that capture and clarify stakeholder expectations. Model-Based Systems Engineering techniques are then applied to define medical system behavior and architecture. Interfaces to other flight and ground systems, and within the medical system are identified and defined. Initial requirements and traceability are established, which sets the stage for identification of future technology development needs. An early approach for verification and validation, taking advantage of terrestrial and near-Earth exploration system analogs, is also defined to further guide system planning and development.

Mindock, Jennifer↗

NASA NTRSDate not supplied

A Case Study on the Challenges and Opportunities for the Deployment of PHM Capabilities in Existing Engineering Systems

The field of Prognostics and Health Management (PHM) of engineering systems has experienced considerable growth over the last decade. From benefits associated with faster and more powerful hardware in the form of wireless sensors, edge devices, and general computing capabilities (GPU’s and cloud computing), to development of powerful algorithms for anomaly detection and remaining useful life (RUL) estimation, the number of engineering systems featuring advanced diagnostics and prognostics capabilities continues to grow at an increasingly faster pace. However, the deployment of PHM capabilities as part of the upgrade of existing engineering systems presents multiple challenges to the PHM practitioner charged with retrofitting such systems. Issues include a lack of specific instrumentation needed to capture the signals of interest; insufficient data and sampling rates required for fault detection and diagnosis, and for detection of failure/degradation indicators; and difficulties in the identification of a system’s nominal behavior as a result of age induced degradation. Today’s PHM practitioner must be able to quickly identify and assess these types of issues to effectively evaluate and select the optimal PHM strategies required to achieve the desired results. This paper presents results from the preliminary evaluation of the High-Pressure Gas Facility (HPGF) infrastructure at NASA’s Stennis Space Center in Hancock County, Mississippi. This evaluation is part of a feasibility study conducted prior to the deployment of prognostics and diagnostics capabilities in the pumps skids of the liquid nitrogen (LN2) system of the HPGF.

Condition Based Maintenance↗

NASA NTRS2016

A Year in Space: Early Results and Lessons Learned from the First Year-Long Expedition Aboard the International Space Station

Two ISS crewmembers recently completed the first year-long orbital stay in two decades. International cooperation was central to the success of Mikhail Kornienko from Russia and Scott Kelly from the United States. Their expedition leveraged current mission experience and capitalized on recent advances in health monitoring technology. This unique effort began in 2012 when the program managers of the ISS partner nations adopted two separate goals: greater multilateral cooperation to increase efficiency of inflight research and a year-long expedition to gain familiarity with in-flight durations approaching that required for a Mars mission. These goals were unified when a set of bilateral Russian and American human research investigations was assigned to the year-long mission, augmented by additional investigations from Europe and Japan. For example, Kelly was assigned 18 investigations (twice the complement on standard six-month missions) including two joint U.S.-Russian studies, and two Russian and two Japanese studies. The core set of American investigations was a repetition of six studies Kelly had done on his previous six-month ISS mission, to allow a direct comparison of physiological and behavioral responses of the longer and shorter durations in this single individual. The remainder of his assignments plus those of Kornienko were drawn from currently active national investigations documenting human adaptation to long-duration spaceflight factors or effectiveness of countermeasures against known deleterious adaptations. The two joint U.S.-Russian investigations were the flagship biomedical studies of the year-long expedition. The "Fluid Shifts" study collocated American research equipment alongside a Russian operational stressor device to document the pattern and impacts of the headward fluid shift long known to occur in weightlessness, including its role in ocular changes recently observed in some astronauts. The "Field Test" study investigated the ability of the astronaut and cosmonaut to perform rudimentary tasks requiring sensorimotor coordination immediately after landing to define human capabilities soon after landing on Mars after an extended transit. Both investigations were highly successful in large part to the thorough integration of the implementation processes of the two partners. Kelly's assignment as the one-year crewmember also provided a serendipitous opportunity for the "Twins Study" comparing changes in his body at the genetic level with those occurring on Earth in his identical twin brother Mark Kelly. Data analysis from this expedition will commence in earnest after frozen in-flight samples are delivered to Earth in May aboard a commercial cargo spacecraft. Detailed results are expected in early 2017. Preliminary results and implementation improvements will be reviewed in this presentation.

Charles, J. B.↗

NASA NTRS2002

Human performance cognitive-behavioral modeling: a benefit for occupational safety

Human Performance Modeling (HPM) is a computer-aided job analysis software methodology used to generate predictions of complex human-automation integration and system flow patterns with the goal of improving operator and system safety. The use of HPM tools has recently been increasing due to reductions in computational cost, augmentations in the tools' fidelity, and usefulness in the generated output. An examination of an Air Man-machine Integration Design and Analysis System (Air MIDAS) model evaluating complex human-automation integration currently underway at NASA Ames Research Center will highlight the importance to occupational safety of considering both cognitive and physical aspects of performance when researching human error.

Occupational Health↗