Results for “continual learning” — Page 16

NASA NTRS2019

Assembly and Integrated Systems Testing for the Flow Boiling and Condensation Experiment (FBCE)

The Flow Boiling and Condensation Experiment (FBCE) is a flight experiment that is designed to operate in the Fluids Integrated Rack (FIR) on the International Space Station (ISS). The objective of the FBCE is to develop an integrated two-phase flow experiment that will serve as a primary platform for obtaining flow boiling and condensation heat transfer data in microgravity. This data will enable the design and analysis of two-phase thermal management systems for future NASA missions that require increased efficiency beyond the current single-phase systems. The FBCE consists of seven modules, each of which must link together mechanically, electrically, and fluidically upon final integration. Five of the modules provide the fluidic and electronic components required to bring the test fluid to the inlet conditions required by the test module, where the primary science is conducted. These inlet conditions encompass fluid quality ranging from a superheated vapor to a subcooled liquid at a variety of flow rates and pressures. The FBCE is currently manufacturing two test modules, each of which has its own separate test campaign with the five support modules. Each of the modules presents a unique challenge in its assembly and checkout, particularly related to ensuring the final hardware configuration can meet the fluid and thermal requirements levied upon them. Following assembly, a series of flight verification tests will be performed, including thermal testing, EMI testing, and final integration in a FIR Ground Integration Unit. This presentation discusses the status of the FBCE as it continues through final verification testing, including current test results and lessons learned throughout the assembly and integration phase of the project. In addition, future options for additional flight test sections will be explored that will benefit the broader NASA thermal and fluids community.

deFiebre, Jesse↗

NASA NTRS2022

Simultaneous Retrieval of Selected Optical Water Quality Indicators From Landsat-8, Sentinel-2, and Sentinel-3

Constructing multi-source satellite-derived water quality (WQ) products in inland and nearshore coastal waters from the past, present, and future missions is a long-standing challenge. Despite inherent differences in sensors’ spectral capability, spatial sampling, and radiometric performance, research efforts focused on formulating, implementing, and validating universal WQ algorithms continue to evolve. This research extends a recently developed machine-learning (ML) model, i.e., Mixture Density Networks (MDNs) (Pahlevan et al., 2020; Smith et al., 2021), to the inverse problem of simultaneously retrieving WQ indicators, including chlorophyll-a (Chla), Total Suspended Solids (TSS), and the absorption by Colored Dissolved Organic Matter at 440 nm (a cdom (440)), across a wide array of aquatic ecosystems. We use a database of in situ measurements to train and optimize MDN models developed for the relevant spectral measurements (400–800 nm) of the Operational Land Imager (OLI), MultiSpectral Instrument (MSI), and Ocean and Land Color Instrument (OLCI) aboard the Landsat-8, Sentinel-2, and Sentinel-3 missions, respectively. Our two performance assessment approaches, namely hold-out and leave-one-out, suggest significant, albeit varying degrees of improvements with respect to second-best algorithms, depending on the sensor and WQ indicator (e.g., 68%, 75%, 117% improvements based on the hold-out method for Chla, TSS, and a cdom (440), respectively from MSI-like spectra). Using these two assessment methods, we provide theoretical upper and lower bounds on model performance when evaluating similar and/or out-of-sample datasets. To evaluate multi-mission product consistency across broad spatial scales, map products are demonstrated for three near-concurrent OLI, MSI, and OLCI acquisitions. Overall, estimated TSS and a cdom (440) from these three missions are consistent within the uncertainty of the model, but Chla maps from MSI and OLCI achieve greater accuracy than those from OLI. By applying two different atmospheric correction processors to OLI and MSI images, we also conduct matchup analyses to quantify the sensitivity of the MDN model and best-practice algorithms to uncertainties in reflectance products. Our model is less or equally sensitive to these uncertainties compared to other algorithms. Recognizing their uncertainties, MDN models can be applied as a global algorithm to enable harmonized retrievals of Chla, TSS, and a cdom (440) in various aquatic ecosystems from multi-source satellite imagery. Local and/or regional ML models tuned with an apt data distribution (e.g., a subset of our dataset) should nevertheless be expected to outperform our global model.

Machine learning↗

NASA NTRSDate not supplied

Cyber Resiliency and the Implementation of a Host-Based Intrusion Detection System in an Urban Air Mobility Environment

With the growth in urban air mobility systems and the increasing reliance on interconnected technologies, ensuring the security of these complex infrastructures has become critical. As cities evolve into smart urban centers, the vulnerability to cyber threats escalates, possibly endangering citizens’ safety and the efficiency of transportation networks. In response to these challenges, this paper presents a study on implementing a Host-Based Intrusion Detection System (HIDS) tailored explicitly to urban mobility environments’ unique demands. This study explores the design, development, and deployment of the proposed HIDS, focusing on its adaptability to monitor the hybrid nature of the urban mobility environment. Leveraging machine learning algorithms and anomaly detection techniques, the HIDS is equipped to continuously monitor and analyze the behavior of individual host systems, vehicles, and devices, thereby providing a proactive approach to threat detection. Implementing a HIDS is a pivotal strategy for enhancing cyber resiliency, as it gives an organization granular visibility into internal system activities, enables rapid detection and response to anomalous behavior and cyber threats, and fortifies the organization’s overall cybersecurity posture. In conclusion, this paper highlights the significance of host-based intrusion detection in urban mobility environments and underscores the necessity of tailored security solutions to safeguard against emerging cyber threats.

UAM↗

NASA NTRSDate not supplied

Adaptive Stress Testing of Collision Avoidance Systems for Small UASs with Deep Reinforcement Learning

The next-generation Airborne Collision Avoidance System for smaller UASs (ACAS sXu) is currently being developed and tested by the Federal Aviation Administration (FAA) to provide detect-and-avoid capability for small unmanned aircraft operating beyond line-of-sight. Due to the complexity and safety-critical nature of the system, safety validation is important not only for the certification of the final system, but also for informing changes during the iterative development process. In this paper, we analyze a prototype of ACAS sXu in simulated aircraft encounters to discover scenarios of small near mid-air collisions (sNMACs), an important safety event in which two aircraft come closer than 50 feet horizontally and 15 feet vertically. Due to the size and complexity of the system as well as rarity of sNMAC events, traditional methods such as Monte Carlo testing often require informed setup and targeting to elicit failures. However, such a dependence on domain knowledge can be incompatible with the independent verification and validation (IV&V) process, the aim of which is to discover unforeseen issues. To address these challenges, we apply an accelerated validation method called adaptive stress testing (AST) to find the most likely sNMAC scenarios without reliance on system introspection. AST uses reinforcement learning to adapt the search towards the most promising areas of the search space as it progresses. We use a state-of-the-art deep reinforcement learning algorithm, proximate policy optimization, to more efficiently search the large and continuous state space. We find that this approach significantly improves the performance of AST compared to a prior approach based on Monte Carlo tree search. We perform experiments using AST to find sNMAC events under various encounter configurations, varying parameters pertaining to dynamics and coordination. Our experiments show AST to be very effective at finding sNMAC scenarios. We summarize our findings, presenting high-level categories of discovered sNMACs and specific examples of encounters in each category.

aircraft collision avoidance↗

NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗

NASA NTRSDate not supplied

A Dynamic PCA and Machine Learning Tool for Automated Identification of Solar Wind Disturbances Impacting Earth’s Magnetosphere

Earth’s magnetosphere is continuously impacted by solar wind and interplanetary magnetic field (IMF) disturbances, such as shocks, discontinuities, magnetic clouds and more. Understanding how such disturbances propagate from the Sun and what is their impact on the different magnetospheric domains is key to understanding and forecasting energy transfer from the solar wind to Earth. The large number of overlapping solar wind and magnetospheric missions carrying magnetometers and the recent advances in communications and data storage technologies have enabled an unprecedented quantity of high-fidelity magnetic field data captured by in-situ spacecraft to be available at the click of a button. However, this massive quantity of available data can prove unwieldy for researchers, limiting the identification of interesting phenomena and disturbances to a relatively small percentage of the total dataset. Several techniques have been previously developed for automated identification of specific types of magnetic anomalies, but these methods are typically mission-specific and can be difficult to generalize. We present initial results for a generic method of automated anomaly detection in magnetic field measurements based on dimensionality reduction and unsupervised clustering via machine learning. The benefit of our technique is its high degree of generalizability and flexibility which make it a most useful data survey tool for a wide range of magnetic field datasets. This method can also be applied simultaneously to other observed time-series properties like plasma density, pressure, and velocity for more accurate event identification. Additionally, the application of this method to data captured by multiple spacecraft enables the simultaneous identification of disturbances and the determination of their propagation characteristics. Initial evaluation of this technique has been performed using data from Magnetospheric MultiScale (MMS) and THEMIS-ARTEMIS missions, providing a testbed scenario for the future Heliophysics Environmental and Radiation Measurement Experiment Suite (HERMES) platform instruments that will measure solar wind and IMF properties from lunar orbit onboard the Gateway station.

Miguel Martinez-Ledesma↗

DOE OSTI2025

Near-zero photon bioimaging by fusing deep learning and ultralow-light microscopy

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near-zero photon bioimaging, a method that operates at kHz rates and 10,000-fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

AI↗

NASA NTRSDate not supplied

Dust Machine Learning Probability and Assessment

- NASA SPoRT introduced the "Dust RGB" via NASA satellites to demonstrate GOES-R ABI capabilities and then evaluated the impact in operations (Fuell et al. 2016) - The Dust RGB allows for continued dust detection at night, but the cooling ground surface limits the effectiveness as night progresses. - SPoRT has developed a 'Machine Learning' (ML) model using a physically-based approach which can correctly label 85% of dust pixels and 99% of no-dust pixels

Dust↗

NASA NTRS2022

Exploring Applications of Machine Learning for Wildfire Monitoring and Detection using Unmanned Aerial Vehicles

Wildfires are increasing in frequency and severity around the world, including the United States. The losses caused by wildfires could be mitigated if high-risk areas, hotspots, and flare-ups could be monitored continuously, such as through the use of Unmanned Aerial Vehicles (UAVs). This paper documents exploratory efforts using machine learning to determine efficient flight paths for UAVs and to detect wildfires using image classification. On path planning, three machine learning techniques—Genetic Algorithm, Simulated Annealing, and Dynamic Programming—were explored. Genetic Algorithm was found to be an effective approach for path planning for wildfire monitoring and surveillance by UAVs. For a scenario of 25 locations in a circular arrangement, the algorithm was able to return the optimal path. The accuracy and execution time was found to be sensitive to the algorithm hyperparameters selected, which was especially evident in scenarios with hundreds or thousands of locations. Simulated Annealing was also found to be an effective approach for UAV path planning, with a major benefit of avoiding getting trapped in local minima and being straightforward to implement. Like Genetic Algorithm, the performance of Simulated Annealing was also found to be sensitive to the algorithm hyperparameters selected. By comparison, Dynamic Programming guarantees optimality for any number of locations, but it was found to be less practical in terms of execution time for scenarios with more than about a couple dozen locations. On wildfire detection, image classification using deep learning with a convolutional neural network was explored. Transfer learning was found to be a useful technique to efficiently train deep learning models. Also, it was determined that GPU processing can increase training speed by an order of magnitude, which enables significantly faster development. For a validation test set of 500 images, there were only two false negatives and zero false positives. These results demonstrate that detecting wildfires in static cameras using machine learning is feasible and establish a baseline for using images captured by UAVs in flight for wildfire detection.

Wildfire management↗

DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

NASA NTRS2015

Design Evolution of the Wide Field Infrared Survey Telescope using Astrophysics Focused Telescope Assets (WFIRST-AFTA) and Lessons Learned

The design of the Wide Field Infrared Survey Telescope using Astrophysics Focused Telescope Assets (WFIRST-AFTA) continues to evolve as each design cycle is analyzed. In 2012, two Hubble sized (2.4 m diameter) telescopes were donated to NASA from elsewhere in the Federal Government. NASA began investigating potential uses for these telescopes and identified WFIRST as a mission to benefit from these assets. With an updated, deeper, and sharper field of view than previous design iterations with a smaller telescope, the optical designs of the WFIRST instruments were updated and the mechanical and thermal designs evolved around the new optical layout. Beginning with Design Cycle 3, significant analysis efforts yielded a design and model that could be evaluated for Structural-Thermal-Optical-Performance (STOP) purposes for the Wide Field Imager (WFI) and provided the basis for evaluating the high level observatory requirements. Development of the Cycle 3 thermal model provided some valuable analysis lessons learned and established best practices for future design cycles. However, the Cycle 3 design did include some major liens and evolving requirements which were addressed in the Cycle 4 Design. Some of the design changes are driven by requirements changes, while others are optimizations or solutions to liens from previous cycles. Again in Cycle 4, STOP analysis was performed and further insights into the overall design were gained leading to the Cycle 5 design effort currently underway. This paper seeks to capture the thermal design evolution, with focus on major design drivers, key decisions and their rationale, and lessons learned as the design evolved.

Thermal Design↗

NASA NTRS2015

Design Evolution of the Wide Field Infrared Survey Telescope Using Astrophysics Focused Telescope Assets (WFIRST-AFTA) and Lessons Learned

The design of the Wide Field Infrared Survey Telescope using Astrophysics Focused Telescope Assets (WFIRST-AFTA) continues to evolve as each design cycle is analyzed. In 2012, two Hubble sized (2.4 m diameter) telescopes were donated to NASA from elsewhere in the Federal Government. NASA began investigating potential uses for these telescopes and identified WFIRST as a mission to benefit from these assets. With an updated, deeper, and sharper field of view than previous design iterations with a smaller telescope, the optical designs of the WFIRST instruments were updated and the mechanical and thermal designs evolved around the new optical layout. Beginning with Design Cycle 3, significant analysis efforts yielded a design and model that could be evaluated for Structural-Thermal-Optical-Performance (STOP) purposes for the Wide Field Imager (WFI) and provided the basis for evaluating the high level observatory requirements. Development of the Cycle 3 thermal model provided some valuable analysis lessons learned and established best practices for future design cycles. However, the Cycle 3 design did include some major liens and evolving requirements which were addressed in the Cycle 4 Design. Some of the design changes are driven by requirements changes, while others are optimizations or solutions to liens from previous cycles. Again in Cycle 4, STOP analysis was performed and further insights into the overall design were gained leading to the Cycle 5 design effort currently underway. This paper seeks to capture the thermal design evolution, with focus on major design drivers, key decisions and their rationale, and lessons learned as the design evolved.

WFIRST↗

NASA NTRS1994

Design and implementation of two two-week Teacher Enhancement Institutes

During this summer, I have been part of a four team effort that planned and executed two two-week Teacher Enhancement Institutes (TEI) for 40 K-8 teachers from this area. The TEI was designed to enhance teachers' background in aeronautics and technology so that they would be better equipped to encourage and to train students in the mathematics, science, and technology fields. The teachers were given a stipend and three graduate credits from Christopher Newport University for their participation in this program. The four ASEE fellows worked together to develop objectives and a schedule of activities for each two-week session based on the program outline given in the grants that were funding this effort. We divided the responsibilities in coordinating and implementing each part of the TEI based on the specific strengths and background of each ASEE fellow. My specific responsibilities were: (1) to develop the course syllabus and generally handle all matters involved with the graduate course; (2) coordinate the follow-up sessions; and (3) design and manage half of the technology sessions that we had scheduled (approximately 30% of the TEI was devoted to technology). Because the first two responsibilities were primarily administrative in nature, I will address only the last. The technology sessions were divided into computer-only and other technologies (e.g., television and digital technology including scanning, digital photography and CD-ROM). I had responsibility for the computer-only technology sessions. The emphasis of these sessions was on use of the Internet specifically to locate and use educational resources. To maximize learning, these sessions were hands-on with two teachers at each computer. Each teacher received instruction in, and actually used, the most popular tools available on the Internet: email (they were given temporary accounts at NASA LaRC), anonymous ftp and archie, gopher and veronica, mosaic, and telnet. Teachers participated in hands-on workshops to learn about these programs, but were also given time during the two-week session to explore on their own and to find resources on the Net that specifically met their needs. In order to ensure that Internet access continues after their return to the classroom, aIl teachers who did not have them also applied for Learning Link accounts (from WHRO, the local public television station) and Virginia Pen accounts (from the Department of Education of Virginia), both of which allow textbased access to Internet. In addition to getting exposure to and practice with Internet tools, teachers were aIso given a hands-on seminar (and also given practice time) on ClarisWorks, an integrated word processing, spreadsheet, database, and paint package. The technology sessions (and TEI as a whole) were enthusiastically received by both new and more experienced teachers as extremely helpful in improving their ability to use technology in developing lesson pIans.

Lambert, Lynn↗

DOE OSTI2024

Fracture Characterization Via AI‐Assisted Analysis of Temperature Logs

Abstract Fractures control fluid flow, mass transport, and heat transfer in a geothermal reservoir. This makes accurate characterization of fracture networks a prerequisite for optimal design and control of a reservoir's exploitation. We develop a deep‐learning procedure to identify fracture locations via interpretation of temporally and spatially continuous downhole temperature measurements. A long short‐term memory fully convolutional network (LSTM‐FCN) is used both to capture long‐term dependencies in sequential temperature data and to distill local features around fractures. A wellbore and fractured‐reservoir thermal model is established to generate temperature data for network training. The trained LSTM‐FCN exhibits a unique ability to detect multiple fractures intersecting a borehole. We use the LSTM‐FCN algorithm to evaluate the effectiveness of different‐stage wellbore temperature measurements on fracture detection in a complex fractured system. Our experiments reveal that the use of various‐stage temperature information as an input feature set improves the robustness of fracture detection to noise interference. This study indicates the practical feasibility of obtaining accurate fracture‐network reconstructions from temperature signals, at reasonable computational cost.

Yang, Xiaoyu↗

DOE OSTI2024

Overview of RFID Applications Utilizing Neural Networks

As Radio Frequency Identification (RFID) methods continue to evolve to higher levels of complexity, one form of machine learning is making its appearance. The use of Neural Networks (NN) in the RFID field is steadily increasing, and in the fields of localization and activity recognition, promising results are being shown from a variety of research. RFID applications fall primarily under two types of problems including regression and classification. We analyze RIFD localization techniques which fall under regression, and activity recognition which falls under classification. Many works don’t classify themselves as activity recognition methods, but because they fall under the classification category, we still consider them as activity recognition techniques. This research overviews the Neural Network models in the localization field based on whether they can perform independently of the environment in which they were tested. For activity recognition and accessory fields, the major methods involve tag-based and tag-free approaches. In conclusion, after the models are surveyed, a comparison study is given to examine what may be the cause for increased accuracy between different Neural Network models.

42 ENGINEERING↗

NASA NTRS2015

Experimental Products Development Team (EPDT) Supporting New AWIPS : Capabilities - Part 2

In 2012, the Experimental Products Development Team (EPDT) was formed within NASA's Short-term Prediction Research and Transition (SPoRT) Center to create training for development of plug‐ins to extend the National Weather Service (NWS) Advanced Weather Interactive Processing System (AWIPS) version 2. The broader atmospheric science community had a need for AWIPS II development training being created at SPoRT and EPDT was expanded to include other groups who were looking for training. Since the expansion of the group occurred, EPDT has provided AWIPS II development training to over thirty participants spanning a wide variety of groups such as NWS Systems Engineering Center, NWS Meteorological Development Laboratory, and several NOAA Cooperative Institutes. Participants within EPDT solidify their learning experience through hands‐on learning and by participating in a "code-sprint" in which they troubleshoot existing and develop plug‐ins. The hands‐on learning workshop is instructor lead with participants completing exercises within the AWIPS II Development Environment. During the code sprints EPDT groups work on projects important to the community and have worked on various plug‐ins such as an RGB image recipe creation tool, and an mPing (crowd sourced precipitation type reporting system) ingest and display. EPDT has developed a well‐defined training regime which prepares participants to fully develop plug‐ins for the extendible AWIPS II architecture from ingest to the display of new data. SPoRT has hosted 2 learning workshops and 1 code sprint over the last two years, and continues to build and shape the EPDT group based on feedback from previous workshops. The presentation will provide an overview of EPDT current and future activities, and best practices developed within EPDT.

Burks, Jason E.↗

NASA NTRS2015

Orion Launch Abort System Performance on Exploration Flight Test 1

This paper will present an overview of the flight test objectives and performance of the Orion Launch Abort System during Exploration Flight Test-1. Exploration Flight Test-1, the first flight test of the Orion spacecraft, was managed and led by the Orion prime contractor, Lockheed Martin, and launched atop a United Launch Alliance Delta IV Heavy rocket. This flight test was a two-orbit, high-apogee, high-energy entry, low-inclination test mission used to validate and test systems critical to crew safety. This test included the first flight test of the Launch Abort System preforming Orion nominal flight mission critical objectives. NASA is currently designing and testing the Orion Multi-Purpose Crew Vehicle (MPCV). Orion will serve as NASA's new exploration vehicle to carry astronauts to deep space destinations and safely return them to earth. The Orion spacecraft is composed of four main elements: the Launch Abort System, the Crew Module, the Service Module, and the Spacecraft Adapter (Fig. 1). The Launch Abort System (LAS) provides two functions; during nominal launches, the LAS provides protection for the Crew Module from atmospheric loads and heating during first stage flight and during emergencies provides a reliable abort capability for aborts that occur within the atmosphere. The Orion Launch Abort System (LAS) consists of an Abort Motor to provide the abort separation from the Launch Vehicle, an Attitude Control Motor to provide attitude and rate control, and a Jettison Motor for crew module to LAS separation (Fig. 2). The jettison motor is used during a nominal launch to separate the LAS from the Launch Vehicle (LV) early in the flight of the second stage when it is no longer needed for aborts and at the end of an LAS abort sequence to enable deployment of the crew module's Landing Recovery System. The LAS also provides a Boost Protective Cover fairing that shields the crew module from debris and the aero-thermal environment during ascent. Although the Orion Program has tested a number of the critical systems of the Orion spacecraft on the ground, the launch environment cannot be replicated completely on Earth. A number of flight tests have been conducted and are planned to demonstrate the performance and enable certification of the Orion Spacecraft. Exploration Flight Test 1, the first flight test of the Orion spacecraft, was successfully flown on December 5, 2014 from Cape Canaveral Air Force Station's Space Launch Complex 37. Orion's first flight was a two-orbit, high-apogee, high-energy entry, low-inclination test mission used to validate and test systems critical to crew safety, such as heat shield performance, separation events, avionics and software performance, attitude control and guidance, parachute deployment and recovery operations. One of the key separation events tested during this flight was the nominal jettison of the LAS. Data from this flight will be used to verify the function of the jettison motor to separate the Launch Abort System from the crew module so it can continue on with the mission. The LAS nominal jettison event on Exploration Flight Test 1 occurred at six minutes and twenty seconds after liftoff (See Fig. 3). The abort motor and attitude control motors were inert for Exploration Flight Test 1, since the mission did not require abort capabilities. A suite of developmental flight instrumentation was included on the flight test to provide data on spacecraft subsystems and separation events. This paper will focus on the flight test objectives and performance of the LAS during ascent and nominal jettison. Selected LAS subsystem flight test data will be presented and discussed in the paper. Exploration Flight Test -1 will provide critical data that will enable engineering to improve Orion's design and reduce risk for the astronauts it will protect as NASA continues to move forward on its human journey to Mars. The lessons learned from Exploration Flight Test 1 and the other Flight Test Vehicles will certainly contribute to the vehicle architecture of a human-rated space launch vehicle.

McCauley, R.↗

NASA NTRS2018

Apollo Missions to the Lunar Surface

Six Apollo missions to the Moon, from 1969-1972, enabled astronauts to collect and bring lunar rocks and materials from the lunar surface to Earth. Apollo lunar samples are curated by NASA Astromaterials at the NASA Johnson Space Center in Houston, TX. Samples continue to be studied and provide clues about our early Solar System. Learn more and view collected samples at: https://curator.jsc.nasa.gov/lunar.

Graff, Paige V.↗