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NASA NTRSDate not supplied

Structures and Materials Research at NASA Langley Research Center

NASA explores the unknown in air and space, innovates for the benefit of humanity, and inspires the world through discovery. The Structures and Materials discipline at NASA Langley Research Center supports this mission through advanced materials and manufacturing approaches applied to innovative structural concepts. We start with synthesizing novel material systems and applying tools and methods to predict and validate their static, dynamic, durability and damage tolerance behavior. We mature materials from test tube to test flight by developing manufacturing techniques that enable the advancement of aerospace structures from concept to reality. We develop advanced measurement techniques for health management and nondestructive evaluation. We draw on our structures and materials core capabilities to enable safe, reliable lightweight aerospace structures for application in all environments. You will find the results of our work in the assembly of large structures in space and on other planets, vehicles that carry crew into space, vehicles that operate at high speeds and in extreme environments, and technology for passenger and crew safety in commercial aviation. This presentation will provide an overview of Langley Research Center’s role within the agency, then focus on how current Structures and Materials research areas support a variety of NASA missions in space operations, exploration, and aeronautics.

Structures↗

NASA NTRS2015

Probabilistic Fatigue Damage Prognosis Using a Surrogate Model Trained Via 3D Finite Element Analysis

Utilizing inverse uncertainty quantification techniques, structural health monitoring can be integrated with damage progression models to form probabilistic predictions of a structure's remaining useful life. However, damage evolution in realistic structures is physically complex. Accurately representing this behavior requires high-fidelity models which are typically computationally prohibitive. In the present work, a high-fidelity finite element model is represented by a surrogate model, reducing computation times. The new approach is used with damage diagnosis data to form a probabilistic prediction of remaining useful life for a test specimen under mixed-mode conditions.

Leser, Patrick E.↗

NASA NTRS2016

Autonomous Operations System: Development and Application

Autonomous control systems provides the ability of self-governance beyond the conventional control system. As the complexity of mechanical and electrical systems increases, there develops a natural drive for developing robust control systems to manage complicated operations. By closing the bridge between conventional automated systems to knowledge based self-awareness systems, nominal control of operations can evolve into relying on safe critical mitigation processes to support any off-nominal behavior. Current research and development efforts lead by the Autonomous Propellant Loading (APL) group at NASA Kennedy Space Center aims to improve cryogenic propellant transfer operations by developing an automated control and health monitoring system. As an integrated systems, the center aims to produce an Autonomous Operations System (AOS) capable of integrating health management operations with automated control to produce a fully autonomous system.

Autonomous Control Systems↗

DOE OSTI2025

Microbiome Comparison and Pathogen Identification for Three Migrating Passerines Captured During Spring Season in Jordan Using 16S rRNA Sequencing

Jordan is located on an important spot along the Mediterranean and Black Sea Flyway. Hundreds of migratory bird species have been identified stopping over in Jordan during spring and autumn migratory seasons. Compared to mammals and economically important birds, the microbiomes of wild bird species are severely understudied. Gut microbial composition is a valuable source of information that reflects food preferences, foraging behavior, and the risk of pathogen transmission to humans and other animals. In this study, we assessed the microbiome composition of three species of migrating passerines (willow warblers, lesser whitethroats, and common reed warblers) captured during the spring migration stopover in Jordan in 2023. A total of 59 fecal samples were selected evenly from the three species and subjected to 16S sequencing and microbiome analysis. Our objectives were to determine the diversity of bacteria in these three species, assess the amount of intra- and inter-specific variation, and detect pathogenic genera and species that could pose health risks to humans, domestic animals, and wildlife. Bacteria mainly belonged to the phyla Proteobacteria (62%), Actinobacteriota (18%), Firmicutes (13%), Cyanobacteria (5%), and Bacteroidota (1%). The results reveal that lesser whitethroats had the greatest variation in bacterial genus richness, Shannon diversity, and microbial composition compared to willow warblers and common reed warblers. The three bird species harbored several pathogenic genera and species, including Campylobacter, Enterococcus, Escherichia-Shigella, Mycoplasma, Rickettsia, Clostridium perfringens, and Vibrio cholerae. We suggest further investigation to understand the relationship between migratory behavior and their gut microbiome. We advocate for the use of advanced molecular techniques to characterize the pathogens found in migratory birds that might have public and environmental health impacts in addition to economic loss.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2018

Health Monitoring and Prognostics for More Electric Aircrafts

As more and more electric vehicles emerge in our daily operation progressively, a very critical challenge lies in the prediction of remaining flying time/distance (for aircraft). This information is important, particularly in the case of unmanned vehicles, because such vehicles can become self-aware, autonomously compute its own capabilities, and identify how to best plan and successfully complete vehicular missions safely. In case of electric aircrafts, computing remaining flying time is also safety-critical, since an aircraft that runs out of battery charge while in the air will eventually lose control leading to catastrophe. In order to tackle and solve the prediction problem, it is essential to have awareness of the current state and health of the system, especially since it is necessary to perform condition-based predictions. To be able to predict the future state of the system, it is also required to possess knowledge of the current and future operations of the vehicle. Given models of the current and future system behavior, the general approach of model-based prognostics can be employed as a solution to the prior stated prediction problem.For electric aircraft, propulsion is based on power generated from batteries. Thus, it is critical to monitor battery state charge and to estimate the ability of the battery to support mission activities as it is being discharged during flight operation. The ability of the vehicle to complete its given mission very much depends on the charge left in the batteries based on its operational route, maneuvering, weather conditions along with aging health of the batteries. Hence, for the purpose this discussion, consider the scenario of an unmanned electric aircraft that has some planned sequence of waypoints to reach throughout its mission. In such a scenario, for this particular aircraft, and within the region it is flown, at most two minutes are required to safely land the aircraft. Thus, it is desired to predict at which point in time the aircraft must begin to head to the runway and land.

Goebel, Kai↗

NASA NTRS2000

Bone Quest - A Space-Based Science and Health Education Unit

This proposal addresses the need for effective and innovative science and health education materials that focus on space bone biology and its implications for bone health on Earth. The focus of these materials, bone biology and health, will increase science knowledge as well as health awareness. Current investigations of the bone loss observed after long-duration space missions provide a link between studies of bone health in space, and studies of osteoporosis, a disease characterized by bone loss and progressive skeletal weakness. The overall goal of this project is to design and develop web-based and print-based materials for high school science students, that will address the following: a) knowledge of normal bone biology and bone biology in a microgravity environment; b) knowledge of osteoporosis; c) knowledge of treatment modalities for space- and Earth-based bone loss; and d} bone-related nutrition knowledge and behavior. To this end, we propose to design and develop a Bone Biology Tutorial which will instruct students about normal bone biology, bone biology in a microgravity environment, osteoporosis - its definition, detection, risk factors, and prevention, treatment modalities for space- and Earth-based bone loss, and the importance of nutrition in bone health. Particular emphasis will be placed on current trends in . adolescent nutrition, and their relationships to bone health. Additionally, we propose to design and develop two interactive nutrition/health ' education activities that will allow students to apply the information provided in the Bone Biology Tutorial. In the first, students will apply constructs provided in the Bone Biology Tutorial to design "Bone Health Plans" for space travelers.

Smith, Scott M.↗

DOE OSTI2022

EPR Investigation of Temporal and Thermal Stability of Irradiated Sweeteners

Abstract Electron paramagnetic resonance was used to study the dosimetric properties of commercial sweeteners (sucrose, saccharin, and stevia). The results concluded that sugar has dosimetric properties of interest, which makes it suitable for dose reconstruction, emergency response, and nuclear forensics applications. The radiation dose response was investigated by irradiating samples up to 10 Gy and found to be linear. The post-irradiation stability of samples was observed to be useful for the intended applications. The results showed that the post-irradiation intensities vary from one sample to another, with the general trend demonstrating a small increase in intensities shortly after irradiation followed by an approximately constant leveling out. Finally, the effect of isochronal annealing was studied by heating samples to various temperatures. The results showed an overall stable behavior of the signal below melting temperature.

Environmental Sciences & Ecology↗

DOE OSTI2020

Breathe Well, Live Well: Implementing an Adult Asthma Self-Management Education Program

Asthma remains a significant health problem in the United States. Adults with poorly controlled asthma can affect their community in a number of ways, from lost productivity in the workplace to health care costs to premature death. Asthma self-management education helps individuals achieve better control of their asthma and is critical for the overall health and well-being of individuals with asthma. While there are numerous programs and initiatives targeting children with asthma, there is a lack of comparable focus on the needs of adults with asthma. The American Lung Association developed Breathe Well, Live Well, an adult asthma self-management education program, and launched it nationwide in 2007. The program for adults has a flexible delivery format for community-based implementation. This article describes the development, dissemination, and transformation of the program. Each stage of implementation showed positive changes in asthma self-management practices that contribute to better asthma control, and one local implementation additionally showed decreased reports of missed work and unscheduled health care visits among participants. The findings from the three evaluations support the use of Breathe Well, Live Well for broad community-based implementation to improve asthma self-management efficacy and behaviors.

Gardner, Emily A.↗

NASA NTRS2007

Space Exploration: Challenges in Medicine, Research, and Ethics

This viewgraph presentation describes the challenges that space exploration faces in terms of medicine, research and ethics. The topics include: 1) Effects of Microgravity on Human Physiology; 2) Radiation; 3) Bone; 4) Behavior and Performance; 5) Muscle; 6) Cardiovascular; 7) Neurovestibular; 8) Food and Nutrition; 9) Immunology and Hematology; 10) Environment; 11) Exploration; 12) Building Block Approach; 13) Exploration Issues; 14) Life Sciences Contributions; 15) Health Care; and 17) Habitability.

Davis, Jeffrey R.↗

NASA NTRS2018

Exploration Medical System Technical Architecture Overview

The Exploration Medical Capability (ExMC) Element Systems Engineering (SE) goals include defining the technical system needed to support medical capabilities for a Mars exploration mission. A draft medical system architecture was developed based on stakeholder needs, system goals, and system behaviors, as captured in an ExMC concept of operations document and a system model. This talk will discuss a high-level view of the medical system, as part of a larger crew health and performance system, both of which will support crew during Deep Space Transport missions. Other mission components, such as the flight system, ground system, caregiver, and patient, will be discussed as aspects of the context because the medical system will have important interactions with each. Additionally, important interactions with other aspects of the crew health and performance system are anticipated, such as health & wellness, mission task performance support, and environmental protection. This talk will highlight areas in which we are working with other disciplines to understand these interactions.

Cerro, J.↗

NASA NTRS2015

The Effects of Long Duration Bed Rest as a Spaceflight Analogue on Resting State Sensorimotor Network Functional Connectivity and Neurocognitive Performance

Long duration spaceflight has been associated with detrimental alterations in human sensorimotor systems and neurocognitive performance. Prolonged exposure to a head-down tilt position during long duration bed rest can resemble several effects of the microgravity environment such as reduced sensory inputs, body unloading and increased cephalic fluid distribution. The question of whether microgravity affects other central nervous system functions such as brain functional connectivity and its relationship with neurocognitive performance is largely unknown, but of potential importance to the health and performance of astronauts both during and post-flight. The aims of the present study are 1) to identify changes in sensorimotor resting state functional connectivity that occur with extended bed rest exposure, and to characterize their recovery time course; 2) to evaluate how these neural changes correlate with neurocognitive performance. Resting-state functional magnetic resonance imaging (rsfMRI) data were collected from 17 male participants. The data were acquired through the NASA bed rest facility, located at the University of Texas Medical Branch (Galveston, TX). Participants remained in bed with their heads tilted down six degrees below their feet for 70 consecutive days. RsfMRI data were obtained at seven time points: 7 and 12 days before bed rest; 7, 50, and 65 days during bed rest; and 7 and 12 days after bed rest. Functional connectivity magnetic resonance imaging (fcMRI) analysis was performed to measure the connectivity of sensorimotor networks in the brain before, during, and post-bed rest. We found a decrease in left putamen connectivity with the pre- and post-central gyri from pre bed rest to the last day in bed rest. In addition, vestibular cortex connectivity with the posterior cingulate cortex decreased from pre to post bed rest. Furthermore, connectivity between cerebellar right superior posterior fissure and other cerebellar regions decreased from pre bed rest to the last day in bed rest. In contrast, connectivity within the default mode network remained stable over the course of bed rest. We also utilized a battery of behavioral measures including spatial working memory tasks and measures of functional mobility and balance. These behavioral measurements were collected before, during, and after bed rest. We will report the preliminary findings of correlations observed between brain functional connectivity and behavioral performance changes. Our results suggest that sensorimotor brain networks exhibit decoupling with extended periods of reduced usage. The findings from this study could aid in the understanding and future design of targeted countermeasures to alleviate the detrimental health and neurocognitive effects of long-duration spaceflight.

Cassady, K.↗

DOE OSTI2024

Bacterial vampirism mediated through taxis to serum

Bacteria of the family Enterobacteriaceae are associated with gastrointestinal (GI) bleeding and bacteremia and are a leading cause of death, from sepsis, for individuals with inflammatory bowel diseases. The bacterial behaviors and mechanisms underlying why these bacteria are prone to bloodstream entry remain poorly understood. Herein, we report that clinical isolates of non-typhoidal Salmonella enterica serovars, Escherichia coli , and Citrobacter koseri are rapidly attracted toward sources of human serum. To simulate GI bleeding, we utilized an injection-based microfluidics device and found that femtoliter volumes of human serum are sufficient to induce bacterial attraction to the serum source. This response is orchestrated through chemotaxis and the chemoattractant L-serine, an amino acid abundant in serum that is recognized through direct binding by the chemoreceptor Tsr. We report the first crystal structures of Salmonella Typhimurium Tsr in complex with L-serine and identify a conserved amino acid recognition motif for L-serine shared among Tsr orthologues. We find Tsr to be widely conserved among Enterobacteriaceae and numerous World Health Organization priority pathogens associated with bloodstream infections. Lastly, we find that Enterobacteriaceae use human serum as a source of nutrients for growth and that chemotaxis and the chemoreceptor Tsr provide a competitive advantage for migration into enterohemorrhagic lesions. We define this bacterial behavior of taxis toward serum, colonization of hemorrhagic lesions, and the consumption of serum nutrients as ‘bacterial vampirism’, which may relate to the proclivity of Enterobacteriaceae for bloodstream infections.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Hybrid Model Based Approaches for Systems Health Management and Prognostics

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Latest achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision making. In principle, data driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for an effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Hybrid Modeling↗

NASA NTRSDate not supplied

Hybrid Approaches to Systems Health Management and Prognostics

Systems Health Management↗

DOE OSTI2024

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models

Abstract Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Toxicology↗

NASA NTRS2003

Extraction of Qualitative Features from Sensor Data Using Windowed Fourier Transform

In this paper, we use Matlab to model the health monitoring of a system through the information gathered from sensors. This implies assessment of the condition of the system components. Once a normal mode of operation is established any deviation from the normal behavior indicates a change. This change may be due to a malfunction of an element, a qualitative change, or a change due to a problem with another element in the network. For example, if one sensor indicates that the temperature in the tank has experienced a step change then a pressure sensor associated with the process in the tank should also experience a step change. The step up and step down as well as sensor disturbances are assumed to be exponential. An RC network is used to model the main process, which is step-up (charging), drift, and step-down (discharging). The sensor disturbances and spike are added while the system is in drift. The system is allowed to run for a period equal to three time constant of the main process before changes occur. Then each point of the signal is selected with a trailing data collected previously. Two trailing lengths of data are selected, one equal to two time constants of the main process and the other equal to two time constants of the sensor disturbance. Next, the DC is removed from each set of data and then the data are passed through a window followed by calculation of spectra for each set. In order to extract features the signal power, peak, and spectrum are plotted vs time. The results indicate distinct shapes corresponding to each process. The study is also carried out for a number of Gaussian distributed noisy cases.

Amini, Abolfazl M.↗

NASA NTRS2014

Damage Characterization of EBC-SiCSiC Ceramic Matrix Composites Under Imposed Thermal Gradient Testing

Due to their high temperature capabilities, Ceramic Matrix Composite (CMC) components are being developed for use in hot-section aerospace engine applications. Harsh engine environments have led to the development of Environmental Barrier Coatings (EBCs) for silicon-based CMCs to further increase thermal and environmental capabilities. This study aims at understanding the damage mechanisms associated with these materials under simulated operating conditions. A high heat-flux laser testing rig capable of imposing large through-thickness thermal gradients by means of controlled laser beam heating and back-side air cooling is used. Tests are performed on uncoated composites, as well as CMC substrates that have been coated with state-of-the-art ceramic EBC systems. Results show that the use of the EBCs may help increase temperature capability and creep resistance by reducing the effects of stressed oxidation and environmental degradation. Also, the ability of electrical resistance (ER) and acoustic emission (AE) measurements to monitor material condition and damage state during high temperature testing is shown; suggesting their usefulness as a valuable health monitoring technique. Micromechanics models are used to describe the localized stress state of the composite system, which is utilized along with ER modeling concepts to develop an electromechanical model capable of characterizing material behavior.

protective coatings (nonmetallic)↗

NASA NTRS2022

Hybrid Model Based Approaches for Systems Health Management and Prognostics

This is a previously approved and published presentation. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Present achievements of data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. Given models of the current and future system behavior, a general approach of model-based prognostics can solve the prediction problem and further decision-making. In principle, data-driven approaches can replace expensive experimental test-setups as well as reduce the number of simulations needed to explore, e.g., the parametric space of a multi-parameter model. Nonetheless, the limitations of pure data-driven methods came to light rather quickly, at least for some industries. In many industrial applications, data acquisition is costly, and the volume of data that can be collected does not satisfy the requirements for effective model training and cross-validation. Therefore, some recent works in the area of machine learning is focusing on blending physics with data-driven algorithms, thus mitigating the drawbacks of the two approaches and emphasizing respective advantages. Partial physical knowledge of the problem can aid the learning process by “guiding” the algorithm towards efficient solutions that satisfy the physics driving the system behavior. The result is a hybrid modeling approach combining physical knowledge as well data-driven methods to develop a unified hybrid approach. A hybrid framework for fusing information from physics-based performance models along with deep learning algorithms for prognostics of complex safety-critical systems is presented. In this framework, physics-based performance models infer unobservable model parameters related to the system's components health solving a calibration problem in the deep learning approach.

Prognostics↗