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NASA NTRS2014

Inspiring the Next Generation: The International Space Station Education Accomplishments

The International Space Station (ISS) has a unique ability to capture the imagination of both students and teachers worldwide. Since 2000, the presence of humans onboard ISS has provided a foundation for numerous educational activities aimed at capturing that interest and motivating study in the sciences, technology, engineering and mathematics (STEM). Over 43 million students around the world have participated in ISS-related educational activities. Projects such as YouTube Space Lab, Sally Ride Earth Knowledge-based Acquired by Middle Schools (EarthKAM), SPHERES (Synchronized Position Hold Engage and Reorient Experimental Satellites) Zero-Robotics, Tomatosphere, and MAI-75 events among others have allowed for global student, teacher and public access to space through student classroom investigations and real-time audio and video contacts with crewmembers. Educational activities are not limited to STEM but encompass all aspects of the human condition. This is well illustrated in the Uchu Renshi project, a chain poem initiated by an astronaut while in space and continued and completed by people on Earth. With ISS operations now extended to 2024, projects like these and their accompanying educational materials are available to more students around the world. From very early on in the program's history, students have been provided with a unique opportunity to get involved and participate in science and engineering projects. Many of these projects support inquiry-based learning that allows students to ask questions, develop hypothesis-derived experiments, obtain supporting evidence and identify solutions or explanations. This approach to learning is well-published as one of the most effective ways to inspire students to pursue careers in scientific and technology fields. Ever since the first space station element was launched, a wide range of student experiments and educational activities have been performed, both individually and collaboratively, by all the international partner agencies, National Aeronautics and Space Administration (NASA), Canadian Space Agency (CSA), European Space Agency, (ESA), Japan Aerospace Exploration Agency (JAXA) and Russian Federal Space Agency (Roscosmos), and a number of non-participating countries, some under commercial agreements. Many of these programs still continue, and others are being developed and added to the stations tasks on a regular basis. These diverse student experiments and programs fall into one of the following categories: student-developed experiments; students performing classroom versions of ISS experiments; students participating in ISS investigator experiments; education competitions; students participating in ISS Engineering Education; Education Demonstrations and Cultural Activities. This paper summarizes some of the main student experiments and educational activities that have been conducted on the space station.

Alleyne, Camille W.↗

NASA NTRS2022

Remote Diagnosis and Operational Response to an In-Flight Failure of the Drill Feed Mechanism Onboard the Mars Science Laboratory Rover

On November 30th, 2016 (Sol 1536 of the mission), the Mars Science Laboratory (MSL) flight operations team commanded the Curiosity rover to drill into a Martian rock to acquire the mission’s 16th rock powder sample. The drilling operation was halted when the drill feed mechanism unexpectedly stalled shortly after disengaging its magnetically-actuated friction brake at the start of a long move to extend the drill bit into contact with the target rock. The drilling campaign was put on hold and later abandoned as diagnostic experimentation revealed a tendency for the actuator to transition in and out of a high-drag state in which the mechanism could not actuate. In the subsequent months, the team conducted a variety of in-flight and ground-based investigations to determine root cause and identify mitigation strategies for continued operation. Mechanism reliability continued to degrade and by June 2017, it became clear that the mechanism could not be relied upon to support drilling operations. The team succeeded in fully extending the feed, enabling the development of new sampling techniques that would not rely on the feed mechanism. The first feed-extended drilling sample was successfully collected on Sol 2057, 521 sols after the initial feed stall. This paper discusses the anomaly investigation process, failure analysis, and feed mechanism operation mitigation strategies. It concludes with key lessons learned about mechanism design, mission architecture, and operational best practices.

Lin, Megan Richardson↗

NASA NTRS1992

Influence of convection on microstructure

The primary motivation for this research has been to determine the cause for space processing altering the microstructure of some eutectics, especially the MnBi-Bi eutectic. Prior experimental research at Grumman and here showed that the microstructure of MnBi-Bi eutectic is twice as fine when solidified in space or in a magnetic field, is uninfluenced by interfacial temperature gradient, adjusts very quickly to changes in freezing rate, and becomes coarser when spin-up/spin-down (accelerated crucible rotation technique) is used during solidification. Theoretical work at Clarkson predicted that buoyancy driven convection on earth could not account for the two fold change in fiber spacing caused by solidification in space. However, a lamellar structure with a planar interface was assumed, and the Soret effect was not included in the analysis. Experimental work at Clarkson showed that the interface is not planar, and that MnBi fibers project out in front of the Bi matrix on the order of one fiber diameter. Originally four primary hypotheses were to be tested under this current grant: (1) a fibrous microstructure is much more sensitive to convection than a lamellar microstructure, which was assumed in our prior theoretical treatment; (2) an interface with one phase projecting out into the melt is much more sensitive to convection than a planar interface, which was assumed in our prior theoretical treatment; (3) the Soret effect is much more important in the absence of convection and has a sufficiently large influence on microstructure that its action can explain the flight results; and (4) the microstructure is much more sensitive to convection when the composition of the bulk melt is off eutectic. As reported previously, we have learned that while a fibrous microstructure and a non-planar interface are more sensitive to convection than a lamellar microstructure with a planar interface, the influence of convection remains too small to explain the flight and magnetic field results. Similarly addition of the Soret effect does not explain the flight and magnetic field results.

Wilcox, William R.↗

NASA NTRS1993

Melt spinning study

Containerless processing of materials provides an excellent opportunity to study nucleation phenomena and produce unique materials, primarily through the formation of metastable phases and deep undercoolings. Deep undercoolings can be readily achieved in falling drops of molten material. Extended solute solubilities and greatly refined microstructures can also be obtained in containerless processing experiments. The Drop Tube Facility at Marshall Space Flight Center has played an important role in enhancing that area of research. Previous experiments performed in the Drop Tube with refractory metals has shown very interesting microstructural changes associated with deep undercoolings. It is apparent also that the microstructure of the deep undercooled species may be changing due to the release of the latent heat of fusion during recalescence. For scientific purposes, it is important to be able to differentiate between the microstructures of the two types of metallic species. A review of the literature shows that although significant advances have been made with respect to the engineering aspects of rapid solidification phenomena, there is still much to be learned in terms of understanding the basic phenomena. The two major ways in which rapid solidification processing provides improved structures and hence improved properties are: (1) production of refined structures such as fine dendrites and eutectics, and (2) production of new alloy compositions, microstructures, and phases through extended solid solubility, new phase reaction sequences, and the formation of metallic-glass microstructures. The objective of this work has been to determine the optimal methodology required to extract this excess energy without affecting the thermo-physical parameters of the under-cooled melt. In normal containerless processing experiments recalescence occurs as the melt returns toward the melting point in order to solidify. A new type of experiment is sought in which the resultant microstructure of the undercooled species is frozen in without going through the melting point regime and subsequent near equilibrium solidification of the remaining liquid. This experimental approach entails the design of an appropriate melt spinning system which is compatible with Drop Tube operations and processing constraints. That work is the goal of this study.

Workman, Gary L.↗

NASA NTRS1999

Countermeasures to Neurobehavioral Deficits from Cumulative Partial Sleep Deprivation During Space Flight

This project is concerned with identifying ways to prevent neurobehavioral and physical deterioration due to inadequate sleep in astronauts during long-duration manned space flight. The performance capability of astronauts during extended-duration space flight depends heavily on achieving recovery through adequate sleep. Even with appropriate circadian alignment, sleep loss can erode fundamental elements of human performance capability including vigilance, cognitive speed and accuracy, working memory, reaction time, and physiological alertness. Adequate sleep is essential during manned space flight not only to ensure high levels of safe and effective human performance, but also as a basic regulatory biology critical to healthy human functioning. There is now extensive objective evidence that astronaut sleep is frequently restricted in space flight to averages between 4 hr and 6.5 hr/day. Chronic sleep restriction during manned space flight can occur in response to endogenous disturbances of sleep (motion sickness, stress, circadian rhythms), environmental disruptions of sleep (noise, temperature, light), and curtailment of sleep due to the work demands and other activities that accompany extended space flight operations. The mechanism through which this risk emerges is the development of cumulative homeostatic pressure for sleep across consecutive days of inadequate sleep. Research has shown that the physiological sleepiness and performance deficits engendered by sleep debt can progressively worsen (i.e., accumulate) over consecutive days of sleep restriction, and that sleep limited to levels commonly experienced by astronauts (i.e., 4 - 6 hr per night) for as little as 1 week, can result in increased lapses of attention, degradation of response times, deficits in complex problem solving, reduced learning, mood disturbance, disruption of essential neuroendocrine, metabolic, and neuroimmune responses, and in some vulnerable persons, the emergence of uncontrolled sleep attacks. The prevention of cumulative performance deficits and neuroendocrine disruption from sleep restriction during extended duration space flight involves finding the most effective ways to obtain sleep in order to maintain the high-level cognitive and physical performance functions required for manned space flight. There is currently a critical deficiency in knowledge of the effects of how variations in sleep duration and timing relate to the most efficient return of performance per unit time invested in sleep during long-duration missions, and how the nature of sleep physiology (i.e., sleep stages, sleep electroencephalographic [EEG] power spectral analyses) change as a function of sleep restriction and performance degradation. The primary aim of this project is to meet these critical deficiencies through utilization of a response surface experimental paradigm, testing in a dose-response manner, varying combinations of sleep duration and timing, for the purpose of establishing how to most effectively limit the cumulative adverse effects on human performance and physiology of chronic sleep restriction in space operations.

Dinges, David F.↗

NASA NTRS1999

Pharmacological Inhibitors of the Proteosome in Atrophying Muscles

It is now clear that the marked loss of muscle mass that occurs with disuse, denervation or in many systemic diseases (cancer cachexia, sepsis, acidosis, various endocrine disorders) is due primarily to accelerated degradation of muscle proteins, especially myofibrillar components. Recent work primarily in Dr. Goldberg's laboratory had suggested that in these diverse conditions, the enhancement of muscle proteolysis results mainly from activation of the Ub-proteasome degradative pathway. In various experimental models of atrophy, rat muscles show a common series of changes indicative of activation of this pathway, including increases in MRNA for Ub and proteasome subunits, content of ubiquitinated proteins, and sensitivity to inhibitors of the proteasome. In order to understand the muscle atrophy seen in weightlessness, Dr. Goldberg's laboratory is collaborating with Dr. Baldwin in studies to define the changes in these parameters upon hind-limb suspension. Related experiments will explore the effects on this degradative system of exercise regimens and also of glucocorticoids, which are known to rise in space personnel and to promote muscle, especially in inactive muscles. The main goals will be: (A) to define the enzymatic changes leading to enhanced activity of the Ub-proteasome pathway in inactive muscles upon hind-limb suspension, and the effects on this system of exposure to glucocorticoids or exercise; and (B) to learn whether inhibitors of the Ub-proteasome pathway may be useful in retarding the excessive proteolysis in atrophying muscles. Using muscle extracts, Dr. Goldberg's group hopes to define the rate-limiting, enzymatic changes that lead to the accelerated Ub-conjugation and protein degradation. They have recently developed cell-free preparations from atrophying rat muscles, in which Ub-conjugation to muscle proteins is increased above control levels. Because these new preparations seem to reproduce the changes occurring in vivo, they will analyze in depth extracts from normal and atrophying muscles to compare the activities of the Ub-activating enzyme (El), the various LTh-carrier proteins (E2s), and Ub-protein ligases (E3s). Recent studies of other types of muscle wasting -suggest a very important role in muscle proteolysis of certain ubiquitination enzymes, E214k and E3-alpha(i.e. components of the "N-end pathway"). Future studies will focus in understanding their role and test whether they are in fact critical for muscle atrophy in vivo. Since weightlessness leads to a specific loss of contractile proteins and to a switching of myosin isotypes, Dr. Goldberg's group will attempt to identify the ubiquitination enzymes specifically involved in myosin degradation both in normal muscle and after hind-limb suspension.

Goldberg, Alfred↗

NASA NTRS1993

Retrieval of ice thickness from polarimetric SAR data

We describe a potential procedure for retrieving ice thickness from multi-frequency polarimetric SAR data for thin ice. This procedure includes first masking out the thicker ice types with a simple classifier and then deriving the thickness of the remaining pixels using a model-inversion technique. The technique used to derive ice thickness from polarimetric observations is provided by a numerical estimator or neural network. A three-layer perceptron implemented with the backpropagation algorithm is used in this investigation with several improved aspects for a faster convergence rate and a better accuracy of the neural network. These improvements include weight initialization, normalization of the output range, the selection of offset constant, and a heuristic learning algorithm. The performance of the neural network is demonstrated by using training data generated by a theoretical scattering model for sea ice matched to the database of interest. The training data are comprised of the polarimetric backscattering coefficients of thin ice and the corresponding input ice parameters to the scattering model. The retrieved ice thickness from the theoretical backscattering coefficients is compare with the input ice thickness to the scattering model to illustrate the accuracy of the inversion method. Results indicate that the network convergence rate and accuracy are higher when multi-frequency training sets are presented. In addition, the dominant backscattering coefficients in retrieving ice thickness are found by comparing the behavior of the network trained backscattering data at various incidence angels. After the neural network is trained with the theoretical backscattering data at various incidence anges, the interconnection weights between nodes are saved and applied to the experimental data to be investigated. In this paper, we illustrate the effectiveness of this technique using polarimetric SAR data collected by the JPL DC-8 radar over a sea ice scene.

Kwok, R.↗

NASA NTRSDate not supplied

Markov Decision Process based Trajectory Planning for UAVs under Uncertain Wind Conditions

In this paper we propose a Markov Decision Process (MDP) algorithm for path-planning of Unmanned Aviation Vehicles (UAVs) under varying wind conditions. Solutions to path-planning for UAVs are becoming increasingly necessary as autonomous UAVs continue to enter commercial and government spaces. Path-planning is inherently challenging, as UAVs needs to account for dynamically changing flying conditions such as weather, obstacle or no-fly zones, degraded vehicle health and off-nominal battery power consumption. Machine learning methods such as Markov Decision Process (MDPs) have the potential to revolutionize how vehicles navigate in such uncertain environments. Previous papers have demonstrated the use of MDPs to optimize UAV path-planning for energy consumption under time-varying wind distribution. In this study, UAV trajectories from a pre-determined waypoint to target cell, will be computed on a 7X7 grid environment by optimizing parameters for mission assurance and safety limits in addition to the energy consumption, and operation time. The UAV navigates the grid by taking actions to move in either of the eight cardinal and intercardinal directions, under constant thrust profile. The next state of the UAV is calculated by considering its action, transition probability, obstacle cells and the wind speed magnitude and direction. Both constant and stochastic wind will be considered in this paper, the parameters being extracted from real wind measurements in proximity to an experimental UAV flight. One of the studies to be demonstrated in this paper is that as the unmanned airspace gets more complex with multiple vehicles and environmental uncertainties, trade-offs between energy consumption, operation time, risk tolerance, and mission assurance needs to be made. Further, MDPs are capable of fast computation of UAV trajectories under varying wind, hence making them suitable for in-flight path planners.

decision-making↗

NASA NTRSDate not supplied

Application of a Bayesian Framework for Plasticity Model Selection

Interpretable Machine Learning (IML) has performed well when tasked with deriving constitutive material models. However, IML has been shown to prefer models that overfit noise in data, which tends to lead to bloat and a decrease in interpretability. Due to these issues, the ability of IML to reliably derive models that fit the data and are both interpretable and generalizable is limited. A method developed recently has shown promise to improve upon traditional IML by using a Bayesian fitness definition for the evolution of free-form models with non-deterministic parameters. This framework was developed for genetic-programming-based symbolic regression(GPSR) and involves model parameter estimation using Sequential Monte Carlo sampling (SMC).The method has demonstrated a reduction in bloat when dealing with noisy data in comparison to conventional GPSR. The results of this framework applied to stress-strain data for copper show models that more effectively predict the experimental data better than was previously shown with GPSR.

plasticity↗

NASA NTRS2001

Enabling Advanced Automation in Spacecraft Operations with the Spacecraft Emergency Response System

True autonomy is the Holy Grail of spacecraft mission operations. The goal of launching a satellite and letting it manage itself throughout its useful life is a worthy one. With true autonomy, the cost of mission operations would be reduced to a negligible amount. Under full autonomy, any problems (no matter the severity or type) that may arise with the spacecraft would be handled without any human intervention via some combination of smart sensors, on-board intelligence, and/or smart automated ground system. Until the day that complete autonomy is practical and affordable to deploy, incremental steps of deploying ever-increasing levels of automation (computerization of once manual tasks) on the ground and on the spacecraft are gradually decreasing the cost of mission operations. For example, NASA's Goddard Space Flight Center (NASA-GSFC) has been flying spacecraft with low cost operations for several years. NASA-GSFC's SMEX (Small Explorer) and MIDEX (Middle Explorer) missions have effectively deployed significant amounts of automation to enable the missions to fly predominately in 'light-out' mode. Under light-out operations the ground system is run without human intervention. Various tools perform many of the tasks previously performed by the human operators. One of the major issues in reducing human staff in favor of automation is the perceived increased in risk of losing data, or even losing a spacecraft, because of anomalous conditions that may occur when there is no one in the control center. When things go wrong, missions deploying advanced automation need to be sure that anomalous conditions are detected and that key personal are notified in a timely manner so that on-call team members can react to those conditions. To ensure the health and safety of its lights-out missions, NASA-GSFC's Advanced Automation and Autonomy branch (Code 588) developed the Spacecraft Emergency Response System (SERS). The SERS is a Web-based collaborative environment that enables secure distributed fault and resource management. The SERS incorporates the use of intelligent agents, threaded discussions, workflow, database connectivity, and links to a variety of communications devices (e.g., two-way paging, PDA's, and Internet phones) via commercial gateways. When the SERS detects a problem, it notifies on-call team members, who then can remotely take any necessary actions to resolve the anomalies.The SERS goes well beyond a simple '911' system that sends out an error code to everyone with a pager. Instead, SERS' software agents send detailed data (i.e., notifications) to the most appropriate team members based on the type and severity of the anomaly and the skills of the on-call team members. The SERS also allows the team members to respond to the notifications from their wireless devices. This unique capability ensures rapid response since the team members no longer have to go to a PC or the control center for every anomalous event. Most importantly, the SERS enables safe experimentation with various techniques for increasing levels of automation, leading to robust autonomy. For the MIDEX missions at NASA GSFC, the SERS is used to provide 'human-in-the-loop' automation. During lights-out operations, as greater control is given to the MIDEX automated systems, the SERS can be configured to page remote personnel and keep them informed regarding actions taking place in the control center. Remote off-duty operators can even be given the option of enabling or inhibiting a specific automated response in near real time via their two-way pagers. The SERS facilitates insertion of new technology to increase automation, while maintaining the safety and security of mission resources. This paper will focus on SERS' overall functionality and how SERS has been designed to handle the monitoring and emergency response for missions with varying levels of automation. The paper will also convey some of the key lessons learned from SERS' deployment across of variety of missions, highlighting this incremental approach to achieving 'robust autonomy'.

Breed, Julie↗

NASA NTRSDate not supplied

High School Citizen Scientists Use AI/ML to Predict Intra-Ocular Pressure From Gene Expression Data for Spaceflown Mice

Artificial Intelligence (AI) and Machine Learning (ML) have increasingly become pivotal in biological and biomedical research, largely due to the culture of open data sharing and its associated benefits. The methodologies inherent in AI/ML are particularly adept at identifying and forecasting biological phenotypes from the vast amounts of data generated by next-generation sequencing technologies. These techniques offer substantial promise for advancing research in space biosciences and for the development of automated systems for monitoring space health. Nevertheless, there are crucial aspects to consider when training, validating, and testing machine learning models in both biological research and clinical contexts. It is essential that Open Science principles, including data sharing and the availability of open-source code, are complemented by high-quality, publicly accessible training resources. These resources should focus on best practices and include modules based on real-world scientific cases and data to ensure that future AI/ML practitioners gain practical experience with genuine problems. Addressing this knowledge gap, we have designed, developed, and delivered both interactive and self-paced training programs for citizen scientists worldwide, enabling them to utilize AI/ML for space biology research. This initiative was made possible through generous funding from a Transformation to Open Science Training grant. The interactive training sessions, conducted this summer, utilized AI/ML techniques to analyze data from the Open Science Data Repository, specifically targeting the effects of spaceflight on ocular structure and function. The dataset OSD-583, from the Rodent Research 9 mission, provides experimental data detailing the ocular responses of mice subjected to a 35-day spaceflight, compared with ground control counterparts. Using OSD-583 as observational data, our summer training participants applied AI/ML methods to predict intraocular pressure from RNA-seq data and identify the genes most predictive of the observed responses. Further analysis through pathway enrichment and gene set enrichment revealed that these genes are involved in molecular and cellular processes contributing to retinal degeneration.

James Casaletto↗

NASA NTRS2004

Lunar and Planetary Science XXXV: Mars: Remote Sensing and Terrestrial Analogs

The session "Mars: Remote Sensing and Terrestrial Analogs" included the following:Physical Meaning of the Hapke Parameter for Macroscopic Roughness: Experimental Determination for Planetary Regolith Surface Analogs and Numerical Approach; Near-Infrared Spectra of Martian Pyroxene Separates: First Results from Mars Spectroscopy Consortium; Anomalous Spectra of High-Ca Pyroxenes: Correlation Between Ir and M ssbauer Patterns; THEMIS-IR Emissivity Spectrum of a Large Dark Streak near Olympus Mons; Geomorphologic/Thermophysical Mapping of the Athabasca Region, Mars, Using THEMIS Infrared Imaging; Mars Thermal Inertia from THEMIS Data; Multispectral Analysis Methods for Mapping Aqueous Mineral Depostis in Proposed Paleolake Basins on Mars Using THEMIS Data; Joint Analysis of Mars Odyssey THEMIS Visible and Infrared Images: A Magic Airbrush for Qualitative and Quantitative Morphology; Analysis of Mars Thermal Emission Spectrometer Data Using Large Mineral Reference Libraries ; Negative Abundance : A Problem in Compositional Modeling of Hyperspectral Images; Mars-LAB: First Remote Sensing Data of Mineralogy Exposed at Small Mars-Analog Craters, Nevada Test Site; A Tool for the 2003 Rover Mini-TES: Downwelling Radiance Compensation Using Integrated Line-Sight Sky Measurements; Learning About Mars Geology Using Thermal Infrared Spectral Imaging: Orbiter and Rover Perspectives; Classifying Terrestrial Volcanic Alteration Processes and Defining Alteration Processes they Represent on Mars; Cemented Volcanic Soils, Martian Spectra and Implications for the Martian Climate; Palagonitic Mars: A Basalt Centric View of Surface Composition and Aqueous Alteration; Combining a Non Linear Unmixing Model and the Tetracorder Algorithm: Application to the ISM Dataset; Spectral Reflectance Properties of Some Basaltic Weathering Products; Morphometric LIDAR Analysis of Amboy Crater, California: Application to MOLA Analysis of Analog Features on Mars; Airborne Radar Study of Soil Moisture at a Mars Analog Site: Tohachi Wash/Little Colorado River; and Antarctic Dry Valleys: Modification of Rocks and Soils and Implications for Mars The Arkaroola Mars Analogue Region, South Australia.

Source record↗

NASA NTRS1999

Solid Surface Combustion Experiment Yields Significant Observations

The spread of a flame over solid fuel is not only a fundamental textbook combustion phenomenon, but also the central element of destructive fires that cause tragic loss of life and property each year. Throughout history, practical measures to prevent and fight fires have been developed, but these have often been based on lessons learned in a costly fire. Since the 1960 s, scientists and engineers have employed powerful tools of scientific research to understand the details of flame spread and how a material can be rendered nonflammable. High-speed computers have enabled complex flame simulations, whereasand lasers have provided measurements of the chemical composition, temperature, and air velocities inside flames. The microgravity environment has emerged as the third great tool for these studies. Spreading flames are complex combinations of chemical reactions and several physical processes including the transport of oxygen and fuel vapor to the flame and the transfer of heat from the flame to fresh fuel and to the surroundings. Depending on its speed, air motion in the vicinity of the flame can affect the flame in substantially different ways. For example, consider the difference between blowing on a campfire and blowing out a match. On Earth, gravity induces air motion because of buoyancy (the familiar rising hot gases); this process cannot be controlled experimentally. For theoreticians, buoyant air motion complicates the problem modeling of flame spread beyond the capacity of modern computers to simulate. The microgravity environment provides experimental control of air motion near spreading flames, with results that can be compared with detailed theory. The Solid Surface Combustion Experiment (SSCE) was designed to obtain benchmark flame spreading data in quiescent test atmospheres--the limiting case of flames spreading. Professor Robert Altenkirch, Vice President for Research at Mississippi State University, proposed the experiment concept, and the NASA Lewis Research Center designed, built, and tested the SSCE hardware. It was the first microgravity science experiment built by Lewis for the space shuttle and the first combustion science experiment flown in space.

Sacksteder, Kurt R.↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗

NASA NTRSDate not supplied

1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Machine learning↗

NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗

NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗

NASA NTRSDate not supplied

Medium-Range River Flood Forecasts Using a Long Short-Term Memory Network

River flooding and the impacts are a concern for decision makers throughout the United States. Accurate medium-range forecasts (~3-7 days) are critical for providing advanced outlooks to emergency management officials. Unfortunately, accurately forecasting rainfall-runoff and the subsequent rise and fall within rivers remain a challenge in hydrological modeling. While complex physical modeling systems are the standard for representing the hydrological processes, they are computationally demanding and can require extensive calibration. Further, uncertainties remain in the model parameters and input data. The use of machine learning can reduce some of the computational demand while maintaining high accuracy. Therefore, this project makes use of a Long Short-Term Memory (LSTM) network which explicitly accounts for the time-dependent nature of rainfall-runoff modeling. The developed LSTM was trained to predict river gauge height, or stage height, based on time-lagged input features which include: gauge height to initialize the model, the NASA Short-term Prediction Research and Transition Center’s instance of the Land Information System (SPoRT-LIS) relative soil moisture to describe the rainfall infiltration rate, and 6-hr Multi-Radar Multi-Sensor quantitative precipitation estimate (MRMS QPE). The developed LSTM based system is then used to produce 7-day forecasts with a 6-hr temporal resolution using three different quantitative precipitation forecasts (QPF) from the NWS’s Weather Prediction Center (WPC), the NCEP Global Forecast System (GFS) model and the National Blend of Models (NBM). This trained modeling system has been implemented as an experimental product at over 100 different rivers in collaboration with at multiple National Weather Service (NWS) Forecast Offices and River Forecast Centers (RFC) across the eastern half of the United States. The developed LSTM model achieved average Nash-Sutcliffe efficiency (NSE) 0.89 higher than the equivalent medium-range National Water Model ensemble member forecast over a 7-day forecast. In addition to the initial development and evaluation, this project has continued to expand. While the initial model was developed for precipitation dominated basins, expansion of the project has taken it to basins effected by snow melt. This presentation will provide an overview of the project with focus on recent developments on incorporating snow melt processes into the model.

Andrew T. White↗