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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The GeneLab Buffet: A Bioinformatic MATRIX of MANGO and TOAST

The GeneLab data repository provides an unparalleled resource for exploring how spaceflight affects organisms with omics-level insights. However, two major interlinked challenges to capitalizing on the information within these data are their vast breadth and the often-specialized expertise that has been required in the past for their analysis. How do you compare responses within and between studies, especially if you are a non-bioinformatics specialist? This presentation will discuss how Space Biology data can be accessed using software to help provide these data resources to address research questions and generate new hypotheses. The presentation will cover a wide range of the available space life science tools but will focus on TOAST, MANGO, the MATRIX, RadBioApp and other interactive relational databases (https://genelab.nasa.gov/external-vis-apps). These exploration environments have been developed to search the GeneLab data repository for new insights that inform how model organisms respond to microgravity, radiation and other factors associated with spaceflight. The presentation will be interactive, and participants will have the opportunity to ask questions and learn more about the data viz and modeling tools that are available to them.

AstroBotany↗
NASA NTRSDate not supplied

Using DIC for Long Slender Structures

High-strain composite deployable structures have been developed for systems such as solar arrays, camera masts or solar sailing propulsion elements. Composite booms in such applications are often flattened and then rolled into a small footprint for low-packaged volume and are then deployed in space. There is a need to obtain deformation for long, slender composite booms on earth through gravity offloading by suspending them vertically and applying distal end (tip) loads. Three-dimensional digital image correlation (3D-DIC), along with other measurement techniques, were used to obtain strain and displacement along the length of a 7.5 m subscale composite Triangular, Rollable, and Collapsible (TRAC) boom in preparation for full scale testing of a 30 m boom. However, incorporating 3D-DIC as a primary measurement tool on long, slender, high-aspect-ratio boom structures presents significant challenges. Challenges include small correlated area due to high aspect ratio, limited standoff distance due to size of test area, coordinate system alignment of multiple camera systems along the length of the boom, nodal mesh extraction for adequate test/analysis correlation, as well as measurement comparison between DIC and other instrumentation used such as fiber optic strain sensing (FOSS) and laser displacement tracking. The contents of the proposed paper will focus on techniques and methods for overcoming the previously mentioned challenges associated with applying 3D-DIC to long, slender boom structures. Results from subscale test along with lessons learned will be discussed.

Deployable Boom↗
NASA NTRS2003

Rapid motor learning in the translational vestibulo-ocular reflex

Motor learning was induced in the translational vestibulo-ocular reflex (TVOR) when monkeys were repeatedly subjected to a brief (0.5 sec) head translation while they tried to maintain binocular fixation on a visual target for juice rewards. If the target was world-fixed, the initial eye speed of the TVOR gradually increased; if the target was head-fixed, the initial eye speed of the TVOR gradually decreased. The rate of learning acquisition was very rapid, with a time constant of approximately 100 trials, which was equivalent to <1 min of accumulated stimulation. These learned changes were consolidated over >or=1 d without any reinforcement, indicating induction of long-term synaptic plasticity. Although the learning generalized to targets with different viewing distances and to head translations with different accelerations, it was highly specific for the particular combination of head motion and evoked eye movement associated with the training. For example, it was specific to the modality of the stimulus (translation vs rotation) and the direction of the evoked eye movement in the training. Furthermore, when one eye was aligned with the heading direction so that it remained motionless during training, learning was not expressed in this eye, but only in the other nonaligned eye. These specificities show that the learning sites are neither in the sensory nor the motor limb of the reflex but in the sensory-motor transformation stage of the reflex. The dependence of the learning on both head motion and evoked eye movement suggests that Hebbian learning may be one of the underlying cellular mechanisms.

Non-NASA Center↗
NASA NTRS1990

Identification of aerospace acoustic sources using sparse distributed associative memory

A pattern recognition system has been developed to classify five different aerospace acoustic sources. In this paper the performance of two new classifiers, an associative memory classifier and a neural network classifier, is compared to the performance of a previously designed system. Sources are classified using features calculated from the time and frequency domain. Each classifier undergoes a training period where it learns to classify sources correctly based on a set of known sources. After training the classifier is tested with unknown sources. Results show that over 96 percent of sources were identified correctly with the new associative memory classifier. The neural network classifier identified over 81 percent of the sources correctly.

Scott, E. A.↗
NASA NTRSDate not supplied

Exploring the Capabilities of a Machine Learning Algorithm to Detect Space Weather-Significant Emerging Active Regions

Active regions are a source of various phenomena responsible for Space Weather disturbances; therefore, developing a technology for early warning about upcoming magnetic activity is crucial to mitigate its impact. However, observational limitations and the high nonlinearity of processes associated with the accumulation of magnetic flux and its interaction with the surrounding plasma during the emergence through the convection zone make early activity detection a challenging problem. To address these challenges, we developed a physics-driven machine learning model that allows us to detect active regions (ARs) before they become visible on the solar surface by analyzing the power spectra of acoustic oscillations observed by the SDO/HMI instrument. This study is based on a time series of Doppler shift maps of 31x31-degree areas tracked with the Carrington rotation rate for four days before and after the emergence. The Doppler shift time series are processed into the oscillation power maps for four frequency ranges and accompanied by line-of-sight magnetograms and the continuum intensity maps from SDO/HMI. The resulting data are converted into a 1D time series representing the mean temporal variations of these quantities. The redacted time series are used as input to predict AR emergence using the Long Short Term Memory (LSTM) method. The training of the LSTM model is based on 40 ARs, which includes an independent analysis for each sub region that exhibits AR emergence or remains quiet. The emergence of magnetic flux (defined as a decrease of the continuum intensity) was detected with the developed LSTM algorithm from 5 to 48 hours before the reported time by NOAA. The developed model is capable of pointing to the time and location of active region formation. In this presentation, we discuss reasons that impact how early in advance the model can identify the upcoming activity and the possibility of improving the current predictive skills and steps to transition to the operational forecast.

Heliophysics↗
NASA NTRS1992

A new neural net approach to robot 3D perception and visuo-motor coordination

A novel neural network approach to robot hand-eye coordination is presented. The approach provides a true sense of visual error servoing, redundant arm configuration control for collision avoidance, and invariant visuo-motor learning under gazing control. A 3-D perception network is introduced to represent the robot internal 3-D metric space in which visual error servoing and arm configuration control are performed. The arm kinematic network performs the bidirectional association between 3-D space arm configurations and joint angles, and enforces the legitimate arm configurations. The arm kinematic net is structured by a radial-based competitive and cooperative network with hierarchical self-organizing learning. The main goal of the present work is to demonstrate that the neural net representation of the robot 3-D perception net serves as an important intermediate functional block connecting robot eyes and arms.

Lee, Sukhan↗
NASA NTRS2012

Use of Remote Sensing Data to Enhance NWS Storm Damage Toolkit

In the wake of a natural disaster such as a tornado, the National Weather Service (NWS) is required to provide a very detailed and timely storm damage assessment to local, state and federal homeland security officials. The Post ]Storm Data Acquisition (PSDA) procedure involves the acquisition and assembly of highly perishable data necessary for accurate post ]event analysis and potential integration into a geographic information system (GIS) available to its end users and associated decision makers. Information gained from the process also enables the NWS to increase its knowledge of extreme events, learn how to better use existing equipment, improve NWS warning programs, and provide accurate storm intensity and damage information to the news media and academia. To help collect and manage all of this information, forecasters in NWS Southern Region are currently developing a Storm Damage Assessment Toolkit (SDAT), which incorporates GIS ]capable phones and laptops into the PSDA process by tagging damage photography, location, and storm damage details with GPS coordinates for aggregation within the GIS database. However, this tool alone does not fully integrate radar and ground based storm damage reports nor does it help to identify undetected storm damage regions. In many cases, information on storm damage location (beginning and ending points, swath width, etc.) from ground surveys is incomplete or difficult to obtain. Geographic factors (terrain and limited roads in rural areas), manpower limitations, and other logistical constraints often prevent the gathering of a comprehensive picture of tornado or hail damage, and may allow damage regions to go undetected. Molthan et al. (2011) have shown that high resolution satellite data can provide additional valuable information on storm damage tracks to augment this database. This paper presents initial development to integrate satellitederived damage track information into the SDAT for near real ]time use by forecasters and decision makers.

Jedlove, Gary J.↗
NASA NTRS2016

Development and Implementation of a Design Metric for Systems Containing Long-Term Fluid Loops

John Steele, a chemist and technical fellow from United Technologies Corporation, provided a water quality module to assist engineers and scientists with a metric tool to evaluate risks associated with the design of space systems with fluid loops. This design metric is a methodical, quantitative, lessons-learned based means to evaluate the robustness of a long-term fluid loop system design. The tool was developed by a cross-section of engineering disciplines who had decades of experience and problem resolution.

Steele, John W.↗
NASA NTRSDate not supplied

Modular Open Systems Approach (MOSA) for a Robust Commercial Lunar Ecosystem

Modular Open Systems Approaches (MOSAs) have been adopted worldwide to solve a myriad of challenges. Open systems by their nature maximize accessibility, reduce barriers to entry, and prevent vendor lock. Similarly, modularity speeds innovation, fosters design re-use, and enables incremental development & growth. Architectures developed using a MOSA synergize the benefits of modularity and open systems to produce better products at lower developmental cost and risk, saving the consumer money while allowing greater developer profit margins. Specific to the challenge of lunar exploration, a MOSA will ensure interoperability among diverse commercial, government, and international partners. Complex spaceflight systems can be assembled, upgraded, and serviced far more easily and with greater commercial involvement with the use of a MOSA. By allowing partners to self-limit their scope to key strengths, they can focus on producing premier components without carrying the high risk and associated cost of auxiliary tasks they don’t specialize in. We survey commercial and Department of Defense (DoD) lessons learned in past decades, discuss DoD robotics’ experience in Iraq and Afghanistan (first without then with MOSAs), and analyze the flourishing commercial ecosystem that has resulted from DoD MOSA adoption. We explore the future of lunar operations to demonstrate the criticality of MOSA adoption in key areas at the right time. Two specific lunar technical fields are studied as examples: Power and In-Situ Resource Utilization (ISRU). Finally, recognizing that the lunar ISRU campaign will be an eminently collaborative operation requiring seamless partnering among diverse commercial, government, and international partners, a comprehensive technology development ecosystem is explored aimed at enabling ISRU in a way that systems and components are affordable for commercial partners to design, build, and sustain.

Mathew DeMinico↗
NASA NTRSDate not supplied

The HySICS Pointing System: Precision Pointing of CLARREO Pathfinder from the ISS

The CLARREO (Climate Absolute Radiance and Refractivity Observatory) Pathfinder (CPF) mission will measure Earth-reflected sunlight with unparalleled accuracy over existing reflected solar (RS) sensors and will also serve as an on-orbit inter-calibration reference to other orbiting sensors. In order to achieve these goals, the HySICS (HyperSpectral Imager for Climate Science) instrument will need to be pointed at a diverse set of targets including: nadir earth, co-aligned earth scans with other orbiting sensors, the Sun, and the Moon. The HySICS Pointing System (HPS) was developed to provide independent pointing at these targets from its mounting location on the ISS. This paper is focused on the HPS and describes: an overview of the CPF mission, an overview of the HPS requirements, the HPS hardware architecture, the various pointing modes that allow the HPS to point at its targets, challenges associated with performing this mission on the ISS, test results from subsystem-level HPS testing, and finally lessons learned that pertain to algorithm/software development and to the benefits of reusing pointing control hardware/architecture from the TSIS-1 mission that also has a 2-axis pointing system on the ISS from the development process.

orbit↗
NASA NTRSDate not supplied

Medical Database Accomplishments and Lessons Learned - 2021

The Medical Database (MD) is a virtual repository consisting of two software components: the Medical Item Database (MedID) and the Evidence Library (EL). MedID consists of engineering data and associated information for specific medical resource items (e.g., pharmaceutical, medical devices, and supporting components), while the EL is a tool which provides all of the medical evidence necessary. The Medical Database will serve as the single “source of truth” for the Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool suite for both medical evidence and medical resource engineering data. It will be used in conjunction with the IMPACT tool suite to inform research prioritizations and perform systematic trade study evaluations using probabilistic risk assessment and simulated human spaceflight missions to aid stakeholders in making informed decisions during Pre-Phase-A planning of human spaceflight missions. The IMPACT project is conducted under the Exploration Medical Capability (ExMC) element of the Human Research Program (HRP), within NASA’s Human Exploration and Operations Mission Directorate. Over the past year, the MedID software has been successfully merged with the Evidence Library into one cohesive Medical Database with two independent user interface experiences for modifying either clinical evidence or resource engineering data. As MD has evolved substantially over the past year, a number of challenges have been encountered and overcome along the way. A number of ‘lessons learned’ and practical/logistical realizations have emerged which will be detailed in the forthcoming Medical Database presentation.

Exploration Medical Capability↗
NASA NTRS1990

Fault diagnosis

The objective of the research in this area of fault management is to develop and implement a decision aiding concept for diagnosing faults, especially faults which are difficult for pilots to identify, and to develop methods for presenting the diagnosis information to the flight crew in a timely and comprehensible manner. The requirements for the diagnosis concept were identified by interviewing pilots, analyzing actual incident and accident cases, and examining psychology literature on how humans perform diagnosis. The diagnosis decision aiding concept developed based on those requirements takes abnormal sensor readings as input, as identified by a fault monitor. Based on these abnormal sensor readings, the diagnosis concept identifies the cause or source of the fault and all components affected by the fault. This concept was implemented for diagnosis of aircraft propulsion and hydraulic subsystems in a computer program called Draphys (Diagnostic Reasoning About Physical Systems). Draphys is unique in two important ways. First, it uses models of both functional and physical relationships in the subsystems. Using both models enables the diagnostic reasoning to identify the fault propagation as the faulted system continues to operate, and to diagnose physical damage. Draphys also reasons about behavior of the faulted system over time, to eliminate possibilities as more information becomes available, and to update the system status as more components are affected by the fault. The crew interface research is examining display issues associated with presenting diagnosis information to the flight crew. One study examined issues for presenting system status information. One lesson learned from that study was that pilots found fault situations to be more complex if they involved multiple subsystems. Another was pilots could identify the faulted systems more quickly if the system status was presented in pictorial or text format. Another study is currently under way to examine pilot mental models of the aircraft subsystems and their use in diagnosis tasks. Future research plans include piloted simulation evaluation of the diagnosis decision aiding concepts and crew interface issues. Information is given in viewgraph form.

Abbott, Kathy↗
NASA NTRS2017

Test and Analysis of Solid Rocket Motor Nozzle Ablative Materials

Asbestos free solid motor internal insulation samples were tested at the MSFC Hyperthermal Facility. Objectives of the test were to gather data for analog characterization of ablative and in-depth thermal performance of rubber materials subject to high enthalpy/pressure flow conditions. Tests were conducted over a range of convective heat fluxes for both inert and chemically reactive sub-sonic free stream gas flow. Instrumentation included use of total calorimeters, thermocouples, and a surface pyrometer for surface temperature measurement. Post-test sample forensics involved measurement of eroded depth, charred depth, total sample weight loss, and documentation of the general condition of the eroded profile. A complete Charring Material Ablator (CMA) style aero-thermal analysis was conducted for the test matrix and results compared to the measured data. In general, comparisons were possible for a number of the cases and the results show a limited predictive ability to model accurately both the ablative response and the in-depth temperature profiles. Lessons learned and modeling recommendations are made regarding future testing and modeling improvements that will increase understanding of the basic chemistry/physics associated with the complicated material ablation process of rubber materials.

Clayton, J. Louie↗
NASA NTRS2017

Arc Jet Test and Analysis of Asbestos Free Solid Rocket Motor Nozzle Dome Ablative Materials

Asbestos free solid motor internal insulation samples were recently tested at the MSFC Hyperthermal Arc Jet Facility. Objectives of the test were to gather data for solid rocket motor analog characterization of ablative and in-depth thermal performance of rubber materials subject to high enthalpy/pressure flow conditions. Tests were conducted over a range of convective heat fluxes for both inert and chemically reactive sub-sonic free stream gas flow. Active instrumentation included use of total calorimeters, in-depth thermocouples, and a surface pyrometer for in-situ surface temperature measurement. Post-test sample forensics involved determination of eroded depth, charred depth, total sample weight loss, and documentation of the general condition of the eroded profile. A complete Charring Material Ablator (CMA) style aero thermal analysis was conducted for the test matrix and results compared to the measured data. In general, comparisons were possible for a number of the cases and the results show a limited predictive ability to model accurately both the ablative response and the in-depth temperature profiles. Lessons learned and modeling recommendations are made regarding future testing and modeling improvements that will increase understanding of the basic chemistry/physics associated with the complicated material ablation process of rubber materials.

Clayton, J. Louie↗
NASA NTRS2016

Analyzing and Predicting Effort Associated with Finding and Fixing Software Faults

Context: Software developers spend a significant amount of time fixing faults. However, not many papers have addressed the actual effort needed to fix software faults. Objective: The objective of this paper is twofold: (1) analysis of the effort needed to fix software faults and how it was affected by several factors and (2) prediction of the level of fix implementation effort based on the information provided in software change requests. Method: The work is based on data related to 1200 failures, extracted from the change tracking system of a large NASA mission. The analysis includes descriptive and inferential statistics. Predictions are made using three supervised machine learning algorithms and three sampling techniques aimed at addressing the imbalanced data problem. Results: Our results show that (1) 83% of the total fix implementation effort was associated with only 20% of failures. (2) Both safety critical failures and post-release failures required three times more effort to fix compared to non-critical and pre-release counterparts, respectively. (3) Failures with fixes spread across multiple components or across multiple types of software artifacts required more effort. The spread across artifacts was more costly than spread across components. (4) Surprisingly, some types of faults associated with later life-cycle activities did not require significant effort. (5) The level of fix implementation effort was predicted with 73% overall accuracy using the original, imbalanced data. Using oversampling techniques improved the overall accuracy up to 77%. More importantly, oversampling significantly improved the prediction of the high level effort, from 31% to around 85%. Conclusions: This paper shows the importance of tying software failures to changes made to fix all associated faults, in one or more software components and/or in one or more software artifacts, and the benefit of studying how the spread of faults and other factors affect the fix implementation effort.

software fix implementation effort↗
NASA NTRSDate not supplied

Can Large Strategic Science Missions Benefit from Class-D/SmallSat Lesson’s Learned?

This presentation is a set of charts presented at the 2024 AIAA ASCEND conference. This report is the result of an activity sponsored by Dr. Wanda Peters, Deputy Associate Administrator of Programs, who tasked Florence Tan and Carolyn Mercer with gathering insights from SmallSat/ Class D missions that could be extrapolated to enhance Class A-C missions.

SmallSats↗
NASA NTRS2006

Assessment of Postflight Locomotor Performance Utilizing a Test of Functional Mobility: Strategic and Adaptive Responses

Space flight induces adaptive modification in sensorimotor function, allowing crewmembers to operate in the unique microgravity environment. This adaptive state, however, is inappropriate for a terrestrial environment. During a re-adaptation period upon their return to Earth, crewmembers experience alterations in sensorimotor function, causing various disturbances in perception, spatial orientation, posture, gait, and eye-head coordination. Following long duration space flight, sensorimotor dysfunction would prevent or extend the time required to make an emergency egress from the vehicle; compromising crew safety and mission objectives. We are investigating two types of motor learning that may interact with each other and influence a crewmember's ability to re-adapt to Earth's gravity environment. In strategic learning, crewmembers make rapid modifications in their motor control strategy emphasizing error reduction. This type of learning may be critical during the first minutes and hours after landing. In adaptive learning, long-term plastic transformations occur, involving morphological changes and synaptic modification. In recent literature these two behavioral components have been associated with separate brain structures that control the execution of motor strategies: the strategic component was linked to the posterior parietal cortex and the adaptive component was linked to the cerebellum (Pisella, et al. 2004). The goal of this paper was to demonstrate the relative contributions of the strategic and adaptive components to the re-adaptation process in locomotor control after long duration space flight missions on the International Space Station (ISS). The Functional Mobility Test (FMT) was developed to assess crewmember s ability to ambulate postflight from an operational and functional perspective. Sixteen crewmembers were tested preflight (3 sessions) and postflight (days 1, 2, 4, 7, 25) following a long duration space flight (approx 6 months) on the ISS. We have further analyzed the FMT data to characterize strategic and adaptive components during the postflight readaptation period. Crewmembers walked at a preferred pace through an obstacle course set up on a base of 10 cm thick medium density foam (Sunmate Foam, Dynamic Systems, Inc., Leicester, NC). The 6.0m X 4.0m course consisted of several pylons made of foam; a Styrofoam barrier 46.0cm high that crewmembers stepped over; and a portal constructed of two Styrofoam blocks, each 31cm high, with a horizontal bar covered by foam and suspended from the ceiling which was adjusted to the height of the crewmember s shoulder. The portal required crewmembers to bend at the waist and step over a barrier simultaneously. All obstacles were lightweight, soft and easily knocked over. Crewmembers were instructed to walk through the course as quickly and as safely as possible without touching any of the objects on the course. This task was performed three times in the clockwise direction and three times in the counterclockwise direction that was randomly chosen. The dependent measures for each trial were: time to complete the course (seconds) and the number of obstacles touched or knocked down. For each crewmember, the time to complete each FMT trial from postflight days 1, 2, 4, 7 and 25 were further analyzed. A single logarithmic curve using a least squares calculation was fit through these data to produce a single comprehensive curve (macro). This macro curve composed of data spanning 25 days, illustrates the re-adaptive learning function over the longer time scale term. Additionally, logarithmic curves were fit to the 6 data trials within each individual post flight test day to produce 5 separate daily curves. These micro curves, produced from data obtained over the course of minutes, illustrates the strategic learning function exhibited over a relative shorter time scale. The macro curve for all subjects exhibited adaptive motor learning patterns over the 25 day period. Howev, 9/16 crewmembers exhibited significant strategic motor learning patterns in their micro curves, as defined by m > 1 in the equation of the line y=m*LN(x) +b. These data indicate that postflight recovery in locomotor function involves both strategic and adaptive mechanisms. Future countermeasures will be designed to enhance both recovery processes.

Warren, L. E.↗